Systems, methods, apparatuses, and devices for identifying and tracking unmanned aerial vehicles via a plurality of sensors

ABSTRACT

Systems, methods, and apparatus for identifying and tracking UAVs including a plurality of sensors operatively connected over a network to a configuration of software and/or hardware. Generally, the plurality of sensors monitors a particular environment and transmits the sensor data to the configuration of software and/or hardware. The data from each individual sensor can be directed towards a process configured to best determine if a UAV is present or approaching the monitored environment. The system generally allows for a detected UAV to be tracked, which may allow for the system or a user of the system to predict how the UAV will continue to behave over time. The sensor information as well as the results generated from the systems and methods may be stored in one or more databases in order to improve the continued identifying and tracking of UAVs.

TECHNICAL FIELD

The present disclosure relates generally to identifying, tracking, andmanaging unmanned aerial vehicles using a plurality of sensors, computerhardware, and computer software.

BACKGROUND

Unmanned Aerial Vehicles (UAVs), often referred to as “drones”, aregenerally aircrafts operated without the presence of a pilot on board.UAVs vary in size and may be controlled in real time from a remotelocation, or configured to operate autonomously. The introduction andgrowing popularity of UAVs in the airspace has raised issues regardinggovernment regulations and the allowable usage of UAVs.

The anonymous nature of UAVs has introduced problems in areas whereaccountability and identity are of the utmost importance. Locations suchas airports, prisons, sporting venues, residential homes, etc., areamong these areas that require a safe and regulated airspace aroundtheir perimeters, and UAVs compromise the ability to ensure the safetyof such airspaces. However, not all UAVs are flown with malicious intentand the use of UAVs to perform various tasks such as delivering consumergoods may become more acceptable as regulations change. Therefore, thereis a long-felt but unresolved need for a system, method, apparatus,and/or device that is designed to detect, identify, track, and monitorUAVs in order to better protect airspaces and the areas they surround aswell as monitor appropriate UAV operations.

BRIEF SUMMARY OF THE DISCLOSURE

Briefly described, and according to one embodiment, aspects of thepresent disclosure relate generally to systems, methods, apparatuses,and devices for identifying, tracking, and managing unmanned aerialvehicles (UAVs) using a plurality of sensors, hardware, and software. Inone embodiment, and in accordance with aspects of the presentdisclosure, a plurality of sensors including at least video, audio,Wi-Fi, and radio frequency (RF) sensors, collect data from theirsurrounding environment in order to detect, identify, track, and manageUAVs.

In one embodiment, the video sensor is configured to “see” anyapproaching objects. In various embodiments the video sensor recordshigh definition video and can detect objects approaching from 100 metersaway (or other predetermined distances based on technical specificationsof the video sensor). According to various aspects of the presentdisclosure, the audio sensor is configured to “listen” to noise andvarious frequencies and/or frequency ranges that may be emitted fromUAVs. In various embodiments, the Wi-Fi sensor included in the pluralityof sensors is configured to detect Wi-Fi signals, and more particularlydetect information transmitted within Wi-Fi signals such as SSID's, MACaddresses, and other information. In one embodiment, the RF sensor isconfigured to monitor frequencies spanning the frequency range of 1 MHzto 6 GHz; however, the RF sensor may be configurable beyond this rangein certain embodiments. In some embodiments, included in the RF sensoris at least one software defined radio (SDR) which allows the RF sensorto be dynamically configurable to monitor any RF frequency and/or rangewithin the radio frequency spectrum.

In various embodiments, the systems, methods, apparatuses, and devicesdescribed herein collect and process large amounts of sensor informationwhich may allow for the system to not only identify and track UAVs, butalso manage a recognizable catalog of UAVs that the system “knows” andmonitors.

In certain embodiments, each sensor may collect its respective data andprocess the data locally within the circuitry of the sensor. In otherembodiments, the sensors merely collect the data and forward the data toa central server which then processes the data.

In one embodiment, a method for identifying unmanned aerial vehicles(UAVs) in a particular air space, comprising the steps of: receivingvideo data from a video sensor, wherein the video data includes at leastone image of an object that may be a UAV flying in an air space;analyzing the video data to determine a first confidence measure thatthe object in the at least one image comprises a UAV; receiving audiosignal data from an audio sensor, wherein the audio signal data includesfrequency data indicating a possible presence of a UAV within the airspace; analyzing the audio signal data to determine a second confidencemeasure that the frequency data comprises a UAV; aggregating the firstconfidence measure and the second confidence measure into a combinedconfidence measure indicating a possible presence of a UAV in the airspace; and, upon determination that the combined confidence measuresexceeds a predetermined threshold value, storing an indication in adatabase that a UAV was identified in the air space.

According to one aspect of the present disclosure, the method, whereinthe video data and audio signal data are analyzed to determineconfidence levels regarding the possible presence of a UAV in theparticular air space, further comprising the steps of: receiving RFsignal data including data indication a possible presence of a UAVwithin the particular air space; analyzing the RF signal data todetermine a third confidence measure that the RF signal data correspondsto a UAV; and, aggregating the third confidence measure into thecombined confidence measure. Additionally, the method, wherein the stepof analyzing the RF signal data to determine the third confidencemeasure further comprises the steps of: filtering the RF signal data toremove one or more unwanted frequencies; decoding the filtered RF signaldata to generate a pattern of one or more frequencies and one or moreamplitudes representing the RF signal data; comparing the pattern of theone or more frequencies and the one or more amplitudes representing theRF signal data to known patterns of frequencies and amplitudes known tobe associated with UAVs; and, upon determination that the pattern of theone or more frequencies and the one or more amplitudes representing theRF signal data substantially matches at least one of the known patterns,determining the third confidence measure. Furthermore, the method,wherein Wi-Fi signal data is received from a particular Wi-Fi sensorproximate to the particular air space, the Wi-Fi signal data includingdata indicating a possible presence of a UAV within the particular airspace. Also, the method, wherein the Wi-Fi signal data is analyzed todetermine a fourth confidence measure that the Wi-Fi signal datacorresponds to a UAV. Additionally, the fourth confidence measure isaggregated into the combined confidence measure.

According to one aspect of the present disclosure, the method, whereinanalyzing the Wi-Fi signal data to determine the fourth confidencemeasure further comprises a step wherein a media access control (MAC)address is extracted from the Wi-Fi signal data. Also, the extracted MACaddress is compared to one or more known MAC addresses known to beassociated with UAVs. Additionally, upon determination that theextracted MAC address substantially matches at least one known MACaddress, a fourth confidence measure is determined. Moreover, in someembodiments, a service set identifier (SSID) is extracted from the Wi-Fisignal data. Furthermore, the extracted SSID is compared to one or moreknown SSIDs known to be associated with UAVs. Also, upon determinationthat the extracted SSID substantially matches at least one known SSID,the fourth confidence measure is determined.

According to one aspect of the present disclosure, the method, whereinthe step of analyzing the Wi-Fi data to determine the fourth confidencemeasure further comprises a step wherein a received signal strengthindicator (RSSI) is extracted from the Wi-Fi signal data. Also, based onthe extracted RSSI, a physical distance of the object emanating theWi-Fi signal data from the particular Wi-Fi sensor is estimated, wherebythe physical distance must be above a predetermined threshold distancevalue to indicate the presence of a UAV.

According to one aspect of the present disclosure, the method, whereinthe step of analyzing the video data to determine a first confidencemeasure further comprises identifying at least one ROI in at least onevideo frame in the video data, the at least one ROI comprising the imageof the object that may be a UAV flying within the particular air space.Also, the method further comprises performing an object classificationprocess with respect to the at least one ROI to determine whether theobject in the image is a UAV. Moreover, the object classificationprocess comprises the steps of; extracting image data from the image ofthe at least one ROI; comparing the extracted image data to prior imagedata of objects known to be UAVs to determine a probability that theobject in the image is a UAV exceeds a predetermined threshold,determine the first confidence measure.

According to one aspect of the present disclosure, the method, whereinthe audio data is analyzed to determine a second confidence measurefurther comprises converting the audio signal data to frequency domaindata such that the audio signal data may be represented as one or morefrequencies. Also, the method further comprises determining if afrequency-to-noise volume ratio for each of the one or more frequenciesis within a predetermined frequency-to-noise threshold range.Additionally, upon determination that a respective frequency-to-noisevolume for respective frequency of the converted audio signal data iswithin the predetermined frequency-to-noise threshold range, the methodfurther comprises comparing the respective frequency to one or more UAVfrequencies known to be associated with UAVs. Also, upon determinationthat the respective frequency substantially matches at least one of theone or more UAV frequencies known to be associated with UAVs, the methodfurther comprises determining the second confidence measure.

According to one aspect of the present disclosure, the method, whereinthe video data and audio signal data are analyzed further comprises thestep of storing the video data and audio signal data in the database inassociation with the indication that the UAV was identified in theparticular air space. Additionally, the method further comprises a stepwherein an alert that a UAV has been detected in the particular airspace is initiated to a system user. Also, the predetermined thresholdvalue comprises a percentage. Furthermore, the particular video sensorand the particular audio sensor are enclosed in a unitary housing.

In one embodiment, a system for identifying unmanned aerial vehicles(UAVs) in a particular air space, comprising: a video sensor proximateto the air space, an audio sensor proximate to the air space, and aprocessor operatively coupled to the video sensor, the audio sensor, anda database. In one embodiment, the video sensor is configured to collectand transmit video data, the video data including at least one image ofan object that may be a UAV flying within the particular air space. In aparticular, the audio sensor is configured to collect and transmit audiosignal data, the audio signal data including at least frequency dataindicating a possible presence of a UAV within the particular air space.In various embodiments, the processor is operative to: analyze the videodata to determine a first confidence measure that the object in the atleast one image comprises a UAV; analyze the audio signal data todetermine a second confidence measure that the frequency data comprisesa UAV; aggregate the first confidence measure and the second confidencemeasure into a combined confidence measure indicating a possiblepresence of a UAV in the particular air space; and, upon determinationthat the combined confidence measure exceeds a predetermined thresholdvalue, store an indication in the database that a UAV was identified inthe particular air space.

In one embodiment, a method for identifying UAVs in an air space via theuse of one or more video sensors, comprising the steps of: receiving avideo frame from a video feed, wherein the video feed was captured by aparticular video sensor proximate to the air space; identifying at leastone region of interest (ROI) in the video frame, wherein the at leastone ROI comprises an image of an object that may be a UAV flying in theair space; performing an object classification process to determinewhether the object in the image is a UAV.

In one embodiment, the object classification process, comprising thesteps of: extracting image data from the image of the at least one ROI;comparing the extracted image data to prior image data to determine aprobability that the object in the image is a UAV; and, upondetermination that the probability that the object in the image is a UAVexceeds a predetermined threshold, denoting the object in the image as aUAV.

According to one aspect of the present disclosure, the method foridentifying UAVs in a particular air space via the use of one or morevideo sensors, further comprising the step of prior to identifying theat least one ROI in the video frame, performing a background subtractionprocess with respect to the video frame. Furthermore, the method,wherein prior to performing the object classification process withrespect to the at least one ROI, confirming that the at least one ROIlies within a predefined attention region indicated as likely to includea UAV. Moreover, the method, wherein prior to performing the objectclassification process with respect to the at least one ROI, confirmingthat the object in the image of the at least one ROI is not part of alearned scene represented by the video frame. Additionally, afterdenoting that the object in the image is a UAV, an attention region isgenerated to encompass the at least one ROI for use in processing ofsubsequent frames to indicate a region likely to include a UAV.

According to one aspect of the present disclosure, the method, whereinprior to the object classification process, performing a scene learningprocess with respect to the at least one ROI to determine whether the atleast one ROI is part of a learned scene represented by the video frame.Furthermore, the method, wherein the scene learning process comprisesthe steps of: comparing the at least one ROI to one or morecharacteristics of one or more stored ROIs, wherein the one or morestored ROIs are associated with substantially stationary objects in theparticular air space; and, upon determination that the at least one ROIdoes not substantially match at least one of the one or more storedROIs, performing the object classification process with respect to theat least one ROI.

According to one aspect of the present disclosure, the method, whereinthe extracted image data comprises RGB color values at one or morelocations within the at least one ROI. Furthermore, the method, whereinthe extracted image data comprises background subtraction data at one ormore locations within the at least one ROI. Additionally, the method,wherein the object classification process further comprises the step ofcomparing the extracted image data to prior image data of objects knownto be non-UAVs in order to determine a probability that the object inthe image is a UAV. Also, the method, wherein the step of denoting theobject in the image as a UAV comprises assigning a confidence level tothe object in the image as UAV, wherein the confidence level comprises astatistical confidence measure that the object in the image is a UAV.Furthermore, the method, wherein the video frame is stored in adatabase.

According to one aspect of the present disclosure, the method, whereinthe frame stored in the database is associated with an indication thatthe object in the image comprises a UAV. Additionally, the ROI comprisesa rectangular shape. Moreover, the method further comprises a stepwherein an alert that a UAV has been detected in the particular airspace is initiated to a system user.

In one embodiment, a system for identifying unmanned aerial vehicles(UAVs) in a particular air space, comprising: one or more video sensorsand a processor operatively coupled to the one or more video sensors. Inone embodiment, the one or more video sensors are proximate to theparticular air space and are configured to collect and transmit a videoframe from a video feed of the particular air space. In a particularembodiment, the processor is operative to: identify at least one regionof interest (ROI) in the video frame, the at least one ROI comprising animage of an object that may be a UAV flying within the particular airspace; and, perform an object classification process with respect to theat least one ROI to determine whether the object in the image is a UAV.In various embodiments, during the object classification process theprocessor is further operative to: extract image data from the image ofthe at least one ROI; compare the extracted image data to prior imagedata of objects known to be UAVs to determine a probability that theobject in the image in a UAV; and, upon determination that theprobability that the object in the image is a UAV exceeds apredetermined threshold, denote the object in the image as a UAV.

In one embodiment, a method for identifying UAVs in an air space via theuse of one or more audio sensors, comprising the steps of: receivingaudio signal data from the air space, wherein the audio signal data iscaptured by an audio sensor; converting the audio signal data tofrequency domain data such that the audio signal data may be representedas one or more frequencies; determining if a frequency-to-noise volumefor each of the one or more frequencies is within a pre-determinedthreshold; upon determination that a respective frequency-to-noisevolume for a respective frequency is within the pre-determinedthreshold, comparing the respective frequency to one or more UAVfrequencies known to be associated with UAVs; and, upon determinationthat the respective frequency substantially matches at least one of theone or more UAV frequencies know to be associated with UAVs, denotingthe respective frequency from the audio signal data as emanating from aUAV.

According to one aspect of the present disclosure, the method, whereinthe step of denoting the respective frequency from the audio signal dataas emanating from a UAV in the particular air space comprises assigninga confidence level to the audio signal data as emanating from a UAV,wherein the confidence level comprises a statistical measure that theaudio signal data emanated from a UAV. Additionally, the method furthercomprises a step wherein the audio signal data is stored in a database.Also, the method further comprises a step wherein the audio signal datais stored in a database with an indication that the audio signal dataemanated from a UAV. Furthermore, the method further comprises a stepwherein an alert to a system user that a UAV has been detected in theparticular air space is initiated.

In one embodiment, a system for identifying unmanned aerial vehicles(UAVs) in particular air space, comprising: one or more audio sensorsproximate to the particular air space and a processor operativelyconnected to the one or more audio sensors. In a particular embodiment,the one or more audio sensors are configured to receive and transmitaudio signal data. In various embodiments, the processor is operativeto: convert the audio signal data to frequency domain data such that theaudio signal data may be represented as one or more frequencies;determine if a frequency-to-noise volume for each of the one or morefrequencies is within a predetermined frequency-to-noise thresholdrange; upon determination that a respective frequency-to-noise volumefor a respective frequency of the converted audio signal data is withinthe predetermined frequency-to-noise threshold range, compare therespective frequency to one or more UAV frequencies known to beassociated with UAVs: and, upon determination that the respectivefrequency substantially matches at least one of the one or more UAVfrequencies known to be associated with UAVs, denote the respectivefrequency from the audio data signal as emanating from a UAV in theparticular air space.

In one embodiment, a method for identifying UAVs via the use of one ormore RF sensors, comprising the steps of: receiving RF signal data;filtering the RF signal data; decoding the RF signal data to generate apattern of one or more frequencies and one or more amplitudesrepresenting the RF signal data; comparing the pattern to known patternsof frequencies and amplitudes known to be associated with UAVs; and,upon determination that the patterns substantially match, denoting thereceived RF signal data as corresponding to a UAV.

According to one aspect of the present disclosure, the method, whereinprior to receiving the RF signal data, the particular RF sensor is tunedto receive signals from a predetermined frequency range. Also, themethod, wherein the predetermined frequency range comprises betweenabout 1 MHz to about 6 GHz. Furthermore, the method, wherein prior tofiltering the RF signal data, it is determined if the signal energyassociated with the RF signal data exceeds a predetermined energythreshold indicative of UAVs.

According to one aspect of the present disclosure, the method, whereinthe one or more unwanted frequencies comprise signal noise or samplingartifacts. Also, the method, wherein the step of denoting the receivedRF signal data as corresponding to a UAV in the particular air spacecomprises assigning a confidence level to the received RF signal data asemanating from a UAV, wherein the confidence level comprises astatistical confidence measure that the RF signal data emanated from aUAV. Furthermore, the method further comprises storing the RF signaldata in a database. Moreover, the method, wherein the RF signal datastored in the database is associated with an indication that the RFsignal data is emanating from a UAV. Additionally, the method furthercomprises initiating an alert to a system user that a UAV has beendetected in the particular air space.

In one embodiment, a system for identifying unmanned aerial vehicles(UAVs) in a particular air space, comprising: a RF sensor and aprocessor operatively coupled to the RF sensor. In a particularembodiment, the RF sensor is proximate to a particular air space and isconfigured to collect and transmit RF signal data from the particularair space. In some embodiments, the processor is operative to: filterthe RF signal data to remove one or more unwanted frequencies; decodethe filtered RF signal data to generate a pattern of frequencies andamplitudes representing the RF signal data; compare the pattern offrequencies and amplitudes representing the RF signal data to knownpatterns of frequencies and amplitudes known to be associated with UAVs;and upon determination that the pattern of frequencies and amplitudessubstantially matches at least one of the known patterns, denote thereceived RF signal data as corresponding to a UAV in the particular airspace.

In various embodiments, the system further comprises a database, and thesystem is further operative to store the received RF signal data in thedatabase. According to various aspects of the present disclosure, theprocessor is further operative to determine whether signal energyassociated with the RF signal data exceeds a predetermined energythreshold indicative of UAVs, wherein the threshold comprises a value ofat least about −86 dBFS. In various embodiments and according toparticular system configurations, the threshold may comprise a value inthe range of about −92 dBFS to about 58 dBFS.

In one embodiment, a method for identifying an aerial object typecorresponding to an aerial object in a particular air space, comprisingthe steps of: receiving a first video frame and a second video framefrom a video feed, wherein the video feed is captured by a particularvideo sensor and wherein the first video frame is captured earlier intime than the second video frame; identifying a first region of interest(ROI) in the first video frame and a second ROI in the second videoframe, the first ROI and the second ROI each comprising an image of anobject; comparing a determined size of the ROI to a determined size ofthe second ROI to determine a delta size parameter between the first ROIand the second ROI; determining whether the delta size parameter iswithin a predetermined size threshold; upon determination that the deltasize parameter is within the predetermined size threshold, comparing adetermined center position of the first ROI to a determined centerposition of the second ROI to determine a spatial location change of theobject within the particular air space; determining whether the spatiallocation change of the object is within a predetermined position changethreshold; and, upon determination that the spatial location change ofthe object is within the predetermined position change threshold,denoting the object in the image as the particular object type.

According to one aspect of the present disclosure, the method, whereinthe particular object comprises an unmanned aerial vehicle (UAV). Also,the method, wherein the first video frame immediately precedes thesecond video frame in the video feed. Furthermore, the method, whereinthe first video frame and/or the second video frame are stored in adatabase. Additionally, the method, wherein the stored first video frameand/or second video frame are associated with an indication that theobject in the image comprises a UAV. Moreover, the first ROI and/or thesecond ROI comprise a rectangular shape. Also, the method furthercomprises initiating an alert to a system user that the particularobject type has been detected in the particular air space.

In one embodiment, a system for identifying an aerial object typecorresponding to an aerial object in a particular air space, comprising:a video sensor and a processor operatively coupled to the video sensor.In various embodiments, the video sensor is proximate to the particularair space and is configured to collect and transmit a video feed.According to aspects of the present disclosure, the video feed includesa first video frame and a second video frame, wherein the first videoframe is captured earlier in time than the second video frame. In aparticular embodiment, the processor is operative to: identify a firstregion of interest (ROI) in the first video frame and a second ROI inthe second video frame, the first ROI and the second ROI each comprisingan image of an object flying within the particular air space; compare adetermined size threshold of the first ROI to a determined sizethreshold of the second ROI to determine a delta size parameter betweenthe ROIs; determine whether the delta size parameter is within apredetermined size threshold; upon determination that the delta sizeparameter is within the predetermined size threshold, compare adetermined center position of the first ROI to a determined centerposition of the second ROI; determine whether the spatial locationchange of the object is within a predetermined position change thresholdexpected for the particular object type; and upon determination that thespatial location change of the object is within the predeterminedposition change threshold, denote the object in the image as theparticular object type.

In one embodiment, the predetermined size threshold is within a range ofabout 100% of the original size of the object to about 1000% of theoriginal size of the object. According to one aspect of the presentdisclosure, the predetermined size threshold comprises a value of atleast about 200% of the original size of the object. In variousembodiments, the predetermined position change threshold is within arange of about 100% of the original location within the frame to about1000% of the original location within the frame. According to one aspectof the present disclosure, the predetermined position change thresholdcomprises a value of at least about 125% change in the position locationwithin the frame. In various embodiments, this percentage in change maybe determined based on pixel change within the frame, relative movementin relation to size of the object, or other appropriate measurements.

These and other aspects, features, and benefits of the claimedinvention(s) will become apparent from the following detailed writtendescription of the preferred embodiments and aspects taken inconjunction with the following drawings, although variations andmodifications thereto may be effected without departing from the spiritand scope of the novel concepts of the disclosure.

BRIEF DESCRIPTION OF FIGURES

The accompanying drawings illustrate one or more embodiments and/oraspects of the disclosure and, together with the written description,serve to explain the principles of the disclosure. Wherever possible,the same reference numbers are used throughout the drawings to refer tothe same or like elements of an embodiment, and wherein:

FIG. 1 is an exemplary operational environment, according to oneembodiment of the present disclosure.

FIG. 2 is an exemplary portrayal of ranges of a plurality of sensors,according to one embodiment of the present disclosure.

FIG. 3 illustrates a top plan view of a structure with a plurality ofdeployed sensors covering a range around the structure, according to oneembodiment of the present disclosure.

FIG. 4 illustrates exemplary system architecture, according to oneembodiment of the present disclosure.

FIG. 5A is an exemplary sensor device, according to one embodiment ofthe present disclosure.

FIG. 5B is an exemplary RF sensor device, according to one embodiment ofthe present disclosure.

FIG. 6 is a flowchart illustrating the general main flow process of thesystem, according to one embodiment of the present disclosure.

FIG. 7 is a flowchart illustrating the video data analysis process,according to one embodiment of the present disclosure.

FIG. 8 is a representative frame from the video data analysis process,according to one embodiment of the present discourse.

FIG. 9 is a flowchart illustrating the scene learning process, accordingto one embodiment of the present disclosure.

FIG. 10 is a flowchart illustrating the object classification process,according to one embodiment of the present disclosure.

FIG. 11 is a flowchart illustrating the audio data analysis process,according to one embodiment of the present disclosure.

FIG. 12 is a flowchart illustrating the radio frequency data analysisprocess, according to one embodiment of the present disclosure.

FIG. 13 is a flowchart illustrating the Wi-Fi data analysis process,according to one embodiment of the present disclosure.

FIG. 14 is a flowchart illustrating the general region of interesttracking process, according to one embodiment of the present disclosure.

DETAILED DESCRIPTION OF FIGURES

For the purpose of promoting an understanding of the principles of thepresent disclosure, reference will now be made to the embodimentsillustrated in the drawings and specific language will be used todescribe the same. It will, nevertheless, be understood that nolimitation of the scope of the disclosure is thereby intended; anyalterations and further modifications of the described or illustratedembodiments, and any further applications of the principles of thedisclosure as illustrated therein are contemplated as would normallyoccur to one skilled in the art to which the disclosure relates. Alllimitations of scope should be determined in accordance with and asexpressed in the claims.

One embodiment of the present disclosure generally relates to systems,methods, apparatuses, and devices configured to identify, track, andmanage UAVs. These and other aspects, features, and benefits of theclaimed invention(s) will become apparent from the following detailedwritten description of the preferred embodiments and aspects taken inconjunction with the following drawings, although variations andmodifications thereto may be effected without departing from the spiritand scope of the novel concepts of the disclosure.

Briefly described, and according to one embodiment, aspects of thepresent disclosure relate generally to systems, methods, apparatuses,and devices for identifying, tracking, and managing unmanned aerialvehicles (UAVs) using a plurality of sensors, hardware, and software. Inone embodiment, and in accordance with aspects of the presentdisclosure, a plurality of sensors including at least video, audio,Wi-Fi, and radio frequency (RF) sensors, collect data from theirsurrounding environment in order to detect, identify, track, and manageUAVs.

In one embodiment, the video sensor is configured to “see” anyapproaching objects. In various embodiments the video sensor record highdefinition video and can detect objects approaching from 100 meters away(or other predetermined distances based on technical specifications ofthe video sensor). According to various aspects of the presentdisclosure, the audio sensor is configured to “listen” to noise andvarious frequencies and/or frequency ranges that may be emitted fromUAVs. In various embodiments, the Wi-Fi sensor included in the pluralityof sensors is configured to detect Wi-Fi signals, and more particularlydetect information transmitted within Wi-Fi signals such as SSID's, MACaddresses, and other information. In one embodiment, the RF sensor isconfigured to monitor frequencies spanning the frequency range of 1 MHzto 6 GHz; however, the RF sensor may be configurable beyond this rangein certain embodiments. In some embodiments, included in the RF sensoris at least one software defined radio (SDR) which allows the RF sensorto be dynamically configurable to monitor any RF frequency and/or rangewithin the radio frequency spectrum.

In various embodiments, the systems, methods, apparatuses, and devicesdescribed herein collect and process large amounts of sensor informationwhich may allow for the system to not only identify and track UAVs, butalso manage a recognizable catalog of UAVs that the system “knows” andmonitors.

In certain embodiments, each sensor may collect its respective data andprocess the data locally within the circuitry of the sensor. In otherembodiments, the sensors merely collect the data and forward the data toa central server which then processes the data.

Referring now to the figures, for the purposes of example andexplanation of the fundamental processes and components of the disclosedsystems, methods, apparatuses, and devices, reference is made to FIG. 1,which illustrates an exemplary, high-level overview of one embodiment ofan operational environment 100 in accordance with various aspects of thepresent disclosure. As will be understood and appreciated, theconceptual overview shown in FIG. 1 represents merely one approach orembodiment of the present system, and other aspects are used accordingto various embodiments of the present system.

In one embodiment, the exemplary operational environment 100 includes atleast an Unmanned Aerial Vehicle Tracking and Monitoring System (UAVTMS)102 and a plurality of installation locations 110A, 110B, and 110C. Invarious embodiments, the UAVTMS 102 is a central system combined with aplurality of sensors and other computer hardware and software operatingto identify, track, and manage UAVs. According to various aspects of thepresent disclosure, the UAVTMS 102 may be referred to herein as thecentral system or the central system and sensors. In particularembodiments, the central system is configured to accept data from theplurality of sensors indicated throughout as element 112, as well asvarious computing devices, databases, and other external sources ofelectronic data. The UAVTMS 102 may be further configured to process thevarious sensor readings and other data through a series of algorithmsand computer implemented processes to identify, track, and manage UAVs.In general, all information from the installation locations may bedirected to the central system of the UAVTMS 102 for processing and insome embodiments the UAVTMS 102 may convert the information from theexternal environments into meaningful data that can be used to furtheridentify and track UAVs.

The disclosed systems, methods, apparatuses, and devices may bedesirable in many situations and scenarios. For example, buildings andstructures such as government buildings, prisons, universities,airports, sporting venues, personal homes, etc., require a safe andmonitored airspace as well as surrounding area. The UAVTMS 102 disclosedherein may allow a plurality of sensors to monitor the airspace andgeneral area surrounding buildings and structures, such as the buildingsand structures mentioned above. Further, as UAVs continue to become morepopular and acceptable in society, it may be desirable to be able todistinguish malicious UAVs (UAVs for spying, trespassing, etc.) frombenign UAVs (UAVs for delivering consumer goods, etc.). In oneembodiment, the UAVTMS 102 disclosed herein may be configured to monitorparticular UAVs and store information regarding particular malicious andbenign UAVs in order to better identify, monitor, and manage theirpresence in an airspace.

In some embodiments, the UAVTMS 102 may include at least a managementmodule 104 and a database 106. As will be described in further detail inFIG. 4, the management module 104 may execute the computer implementedmethods of processing data inputs and outputs, as well as analyzingwhether or not an object is a UAV and further determine if it should betracked, monitored, or otherwise responded to in another appropriatemanner. The management module 104 may include hardware components suchas a processor, computer executable instructions, a non-transitorycomputer readable medium wherein the computer executable instructionsmay be stored, etc. In the present embodiment, the management module 104may share a bi-directional communication link with a database 106 whichmay allow for the two elements to send and receive data across thecommunication link as necessary. The database 106 included in the UAVTMS102 may store any information pertaining to the processes performed bythe management module 104. Examples of this information may include butare not limited to images of previously identified UAVs, audio filesincluding data representing sound patterns of UAVs, information aboutobjects that resemble UAVs but should not be mistaken for one, etc.According to various aspects of the present disclosure, the centralsystem of the UAVTMS 102 may include modules such as the managementmodule 104. Also, the management module 104 may include various servers,databases, and other computing hardware located either in a remote orcentral location. In one embodiment, the central system may operate as acloud computing system. In other embodiments, the central system may bephysically located in close proximity to the installation locations.

Continuing with FIG. 1 and as mentioned above, in some embodiments theUAVTMS 102 may be deployed at a plurality of installation locations,indicated throughout as 110A-110C, through networks 108. The networks108 may be, but are not limited to the Internet, and may involve theusage of one or more services (e.g., a Web-deployed service withclient/service architecture, a corporate Local Area Network (LAN) orWide Area Network (WAN), a cellular data network, or through acloud-based system). Moreover, as will be understood and appreciated byone having ordinary skill in the art, various networking components likerouters, switches, hubs, etc. are typically involved in thesecommunications. Although not shown in FIG. 1, such communications mayinclude, in various embodiments, one or more secure networks, gateways,or firewalls that provide additional security from unwarrantedintrusions by unauthorized third parties and cyber-attacks.

As shown in the present embodiment, examples of installation locationsmay include airports 110A, prisons 110B, and residential homes 110C,whereby the installation locations 110A-110C may send and receive dataover networks 108 to the central system of the UAVTMS 102. In someembodiments, the installation locations 110A-110C may provide themajority of data accepted by the UAVTMS 102. It should be understood bythe discussion herein that the present disclosure should not be limitedto installation locations described.

According to aspects of the present disclosure, the installationlocations such as airports 110A, prisons 110B, residential homes 110C,or other structures and buildings may include a plurality of sensors112A-112C deployed on the structure or building that communicate withthe central server of the UAVTMS 102 over a network 108. In someembodiments, the plurality of sensors may communicate the sensorreadings, over the network 108, to the UAVTMS 102 to be processed. Inother embodiments, the sensor readings may be processed locally beforebeing sent to the UAVTMS 102. In an example scenario, a UAV may beapproaching a fenced enclosure adjacent to a prison 110B. This scenariomay present a risk to the prison 110B, because the UAV may be carrying apayload that could be dangerous if it were to be delivered to a prisoninmate. The plurality of sensors 112B deployed on the prison 110B mayidentify and track the UAV before it has the opportunity to drop thepayload onto prison grounds or present a risk in another situation. Inone embodiment, the UAVTMS 102 may identify and track the approachingUAV and alarm the prison guards to escort any inmates back into theprison 110B. In another embodiment, the prison 110B may exerciseforceful action against the UAV, which may include overtaking the UAV'scontrol system or disabling the UAV's ability to remain airborne. Inother embodiments, the central system of the UAVTMS 102 operating at theprison 110B may simply track the UAV, and manage the UAV's identitywithin the central system of the UAVTMS in order to more easilyrecognize the UAV if it were to re-appear in the future.

In various embodiments, the sensors included in the plurality of sensors112A-112C may be proprietary sensors or commercially available sensors.In particular embodiments, the video sensor included in the plurality ofsensors 112A-112C is similar to the Lensation GmbH Lensagon B10M5425. Inone embodiment, the video sensor has a dome-shaped configuration and iscapable of recording 1080p resolution video within a wide angled fieldof view. According to various aspects of the present disclosure, thevideo sensor is configured to record activity within a field of viewthat a UAV would be expected to enter. For example, the video sensor maybe pointed upward at the sky in anticipation of a UAV approaching from ahigh altitude. In some embodiments, a pre-installed stand-alone videosensor, such as pre-existing home/location security equipment, can beincluded in the plurality of sensors 112A-112C.

In various embodiments, the audio sensor may be a proprietary waterproofaudio sensor designed to receive, amplify, and convert sound fromaudible vibrations to digital representations of a signal byimplementing an analog to digital converter. According to variousaspects of the present disclosure, the audio sensor may be capable of24-bit sampling at various rates, such as 192 kHz.

In one embodiment, the Wi-Fi sensor may operate similarly to the IntelCorporation Dual Band Wireless-AC 3160 Wi-Fi card. In variousembodiments, the Wi-Fi sensor is configured to detect wireless signalsand more particularly Wi-Fi signals transmitting information such asService Set Identifiers (SSID), Media Access Control (MAC) addresses,Received Signal Strength Indicators (RSSI), and other informationregarding potential UAVs.

In various embodiments, the RF sensor may operate similarly to the GreatScott Gadgets HackRF One sensors. In various embodiments, the RF sensoris configurable to operate within the 1 MHz to 6 GHz frequency range. Inparticular embodiments, the RF sensor may be configured to operatewithin any appropriate frequency range as defined by the particularhardware and software in operating on the device.

In one embodiment, a user 118 operates a computing device connected tothe central system of the UAVTMS 102 over the network 108. According tovarious aspects of the present disclosure, the user 118 may be amoderator or manager of a particular installation location 110A-110C. Insome embodiments, the user 118 may be able to interact with or monitorthe plurality of sensors 112A-112C at the installation locations110A-110C. In an example scenario, a user may have a plurality ofsensors 112C deployed on his/her home 110C and would like to monitorhis/her surrounding property while away. Using a computing device suchas a mobile phone, the user 118 could access the information regardingthe plurality of sensors 112C deployed on his/her home 110C by logginginto the central system of the UAVTMS 102 and accessing a control panelor dashboard. In various embodiments, accessing the control panel ordashboard allows for the user 118 to manage the plurality of sensors112C at the installation location 110C as well as view real-time feedsfrom the video sensor, historical data from previous UAVs or non-UAVsthat were detected by the central system and sensors 102, current mapsrepresenting particular sensor ranges, individual sensor diagnostics,and other relevant information regarding the identifying, tracking,monitoring, and managing of UAVs. In particular embodiments, there maybe multiple deployments of a plurality of sensors 112C on theinstallation location 110C. In accordance with aspects of the presentdisclosure, the user 118 may manage multiple deployments of sensors 112Con one installation location 110C from the portal or dashboard. Also,the user 118 may manage multiple installation locations 110C from theportal or dashboard.

In some embodiments, the user 118 may use a computing device in order toaccess a web server or web application that may allow access to thecentral system of the UAVTMS 102. It should be understood from thediscussion herein that any type of computing device such as a tablet,laptop computer, desktop computer, mobile phone, etc., could be used toaccess the central system of the UAVTMS 102 and the present disclosureshould not be limited to the use of just a mobile phone.

In one embodiment, third party databases and data sources 120 areconnected to the central system of the UAVTMS 102 over a network 108.These third party databases 120 may include a plurality of differentdatasets and sources of information pertinent to identifying andtracking UAVs, or maintaining a system as described in the presentdisclosure. As necessary, the central system of the UAVTMS 102 may writeand read data to and from the third party databases 120. In variousembodiments, it may be beneficial for the central system of the UAVTMS102 to access information regarding UAV manufacturers and specificationsin a third party database 120 in order to cross reference and verify thedata collected by the plurality of sensors 112A-112C with themanufacturer's information. In a scenario where a UAV is approaching anairport 110A and the airport 110A has deployed a plurality of sensors112A such as those described herein, the plurality of sensors 112A maybe able to read signals from the approaching UAV and compare them tosignals known to be emitted from certain UAVs of particularmanufacturers. In other embodiments, the plurality of sensors 112A maytransmit the detected signals to the central system of the UAVTMS 102 inorder to compare the signals to other signal known to be emitted fromcertain UAVs of particular manufacturers. That information may allow theairport 110A to make an informed decision regarding how to respond tothe approaching UAV. In some embodiments, information similar to theinformation available from third party databases 120 may already bestored in a database 106 included in the UAVTMS 102. However, includingaccess to third party databases 120 may allow for the UAVTMS 102, aswell as all parts of the disclosed system, to have access to the mostrecent information available in real time.

In the present embodiment, a third party database 120 is shown includingrelevant data and information 122 corresponding to but not limited toUAV updates, regulations, manufacturer specifications, and other generalinformation. In one embodiment, this relevant data and information 122may allow for the central system of the UAVTMS 102 to have access todata that may determine how the system may respond to UAVs. For example,the Federal Aviation Administration (FAA) may release new regulationsregarding how UAVs may be operated in certain areas. This informationmay then automatically be updated in the third party database 120. Thisupdated information may change how the system responds to a detected UAVflying at a certain height if operating a UAV at that height is madeillegal based on new regulations.

In various embodiments, the relevant data and information 122 mayinclude information pertaining to particular UAVs such as MAC addresses,particular communication frequencies, noise patterns, and othermanufacturer-specific information regarding UAVs. By accessing the dataand information 122 included in the third party database 120, thecentral system of the UAVTMS 102 may be able to more consistently andaccurately identify, track, monitor, and manage UAVs.

Still referring to FIG. 1, in one embodiment, the plurality of sensors112A-112C may be combined into one all-encompassing device. Devices suchas those shown in FIGS. 5A and 5B may include the plurality of sensors112A-112C described in the discussion herein. According to variousaspects of the present disclosure, the plurality of sensors 112A-112Cmay be a single sensor or many sensors enclosed in either one moremultiple devices. Now referring back to FIG. 1, a device 112A may beinstalled on the air traffic control tower of the airport 110A in thepresent embodiment. In one embodiment, a device range 114A, representedas dotted lines and propagating from the device 112A, indicates therange that the device 112A may be able to detect UAVs within. In variousembodiments, having a plurality of sensors included in one device mayallow the ranges of each sensor to originate from the same location. Insome embodiments, it may be beneficial to have a plurality of sensorsincluded in one device and other sensors as stand-alone sensors if aparticular area needs specific or customized coverage. In particularembodiments, the airport 110A may require multiple devices 112A in orderto sufficiently cover a desired area or range. According to variousaspects of the present disclosure, any appropriate sensor may beincluded in the device 112A, and the present disclosure should not belimited to the sensors listed and described.

In the present embodiment and continuing with the airport 112A externalenvironment, a UAV 116A is shown within the dotted lines representingthe device range 114A. According to aspects of the present disclosure,the UAV 116A may be detectable by one or more sensors included withinthe device 112A when the UAV 116A enters the device range 114A. Oncewithin the device range 114A, the device 112A may transmit informationregarding the UAV 116A to the central system of the UAVTMS 102 forprocessing. In certain embodiments, the device 112A may process thesensor readings locally. Once the information regarding the UAV 116A isprocessed by the UAVTMS 102, the UAVTMS 102 may then decide how torespond to the UAV 116A. Also in the present embodiment is an airplane118A flying near the airport 110A. In one embodiment, the airplane 118Amay enter a device range 114A. Similarly to when the UAV 116A enters thedevice range 114A, when the airplane 118A enters the device range 114Athe device 112A may transmit information regarding the airplane 118A tothe central system of the UAVTMS 102 for processing, or the processingmay occur locally at the device 112A. As will be discussed in greaterdetail herein, when the UAV 116A and the airplane 118A are detectedwithin the device range 114A, the central system and sensors in generaldo not know or have not confirmed the identity of these objects, but theUAVTMS 102 can quickly identify each object as a UAV or non-UAV byimplementing the various systems and methods described in the presentdisclosure.

Continuing with FIG. 1 and according to aspects of the presentdisclosure, the devices 112A-112C can be used in many environments andinstallation locations in addition to those discussed herein. In variousembodiments, devices such as 112A-112C may be deployed at locations suchas hospitals, office buildings, universities, sporting venues, etc.

Turning now to FIG. 2, an exemplary portrayal 200 of sensor rangesaround a location (e.g., an airport 110A) is shown according to oneembodiment of the present disclosure. In the present embodiment, a RFsensor range 202A, video sensor range 202B, Wi-Fi sensor range 202C, andan audio sensor range 202D surround the airport 110A and may bepropagated from a device 112A including the sensors. In the presentembodiment, the device 112A is shown included on the air traffic controltower, but it should be understood from the discussion herein that thedevice 112A, or many devices 112A, may be deployed anywhere in or aroundthe airport 110A. As will be described further below in the detaileddescription of FIG. 2, combining data from a plurality of sensors allowsfor the UAVTMS 102 to quickly identify a UAV in an area that may containvarious non-UAV objects such as birds and planes that may triggertypical aerial monitoring devices.

In an environment such as the one shown in the present embodiment, itmay be important to monitor and control the surrounding airspace. Asituation may arise where a UAV is flying near the airport 110A runwayand may strike an airplane, potentially causing damage to the airplaneand risking the lives of the passengers. Another situation may arisewhere a particular military aircraft is intended to remain concealedwithin the confines of the airport 110A, and a UAV equipped with acamera may recognize the aircraft, resulting in a national securitythreat. In one embodiment, the RF, video, Wi-Fi, and audio sensors mayall be configured to monitor their surroundings and prevent the abovescenarios. For example, in the present embodiment a UAV 204A has enteredthe RF sensor range 202A, and therefore the UAV 204A may be detectableby the RF sensor. Also in the present embodiment, a UAV 204C has enteredthe RF sensor range 202A, video sensor range 202B, and the Wi-Fi sensorrange 202C Wi-Fi, and therefore the UAV 204C may be detectable by eachof those three sensors. In particular embodiments, if an object isdetectable by multiple sensors, it may allow for the UAVTMS 102 todetermine if it is a UAV faster than if the object was only detected byone sensor. According to various aspects of the present disclosure, eachsensor is capable of monitoring the airspace between the sensor and itsfarthest extendable range.

In various embodiments, not all types of sensors are capable ofextending equivalent ranges. According to aspects of the presentdisclosure, the ranges of the plurality of sensors may overlap untileach sensor has reached it maximum range. For example, in the presentembodiment, only the RF sensor is capable of detecting UAVs at itsoutermost range 202A, and all deployed sensors are capable of detectingUAVs at the audio sensor's outermost range 202D. In particularembodiments, overlapping sensor ranges may allow for the central systemand sensors 102 to better identify and determine a UAV from a non-UAVsuch as a plane or a bird. However, according to various aspects of thepresent disclosure, the sensor ranges 202A-202D are not required tooverlap, and some areas may be better monitored by using one particularsensor. In various embodiments, the described sensor ranges 202A-202Dmay vary from the current embodiment. For example, it is possible thatthe audio sensor range 202D may extend farther than the Wi-Fi range 202Cbased on configuration, hardware specifications, etc. Also, in oneembodiment the video sensor may be configured to accept differentlenses. Allowing the video sensor to accept different lenses may allowfor the video sensor to record a larger field of view, record withincreased clarity/resolution at farther distances, etc. In particularembodiments, certain sensor configurations allow for a wide spherical ordome-like range, while other sensor configurations monitor a moredirected field of view. The present embodiment is only one configurationof sensor ranges and it should be understood from the discussion hereinthat there may be many configurations of different sensors and sensorranges, and the examples shown herein are exemplary and for the purposeof discussion only.

FIG. 3 is a top plan view 300 of multiple buildings or structures at theprison installation location 110B with a plurality of devices 112Bdeployed thereon. In the present embodiment, the range 114B anddirection of the sensor coverage is indicated by dashed linespropagating from the devices 112B. In one embodiment, this range 114Band direction may represent the area around a building or structure atthe installation location 110B in which a UAV would be detectable. Insome embodiments, each device 112B may be installed at certain anglesand configurations in order to monitor a range 114B or a particularfield of view or area. In particular embodiments, devices 112B areconfigured to monitor certain ranges 114B by taking into accountvulnerable areas such as large open spaces around the installationlocation 110B, and other factors such as particular shapes and sizes ofbuildings in order to ensure that unnecessary amounts of coverage arenot directed at locations that require less coverage, etc. According tothe present embodiment, each device 112B may have a general range 114B,indicated by the dotted lines propagating from the devices 112B, whereinif a UAV were to enter then that UAV would be detectable. As mentionedpreviously in FIG. 2, each device 112B may be configured to includedifferent sensors and different ranges 114B. This is shown, according toone embodiment, by the various device ranges 114B shown in the presentembodiment. In various embodiments, one device range 114B may be twiceas large as another device range 114B due to either particularconfigurations, the number of sensors included in the device 112B, thequality and specifications of the particular sensors included, etc.These devices 112B may have been configured to monitor particular areassurrounding the structures 110B in such a way that the area of coverageof all ranges 114B may be maximized. In various embodiments, bystrategically choosing the location of installation for each device112B, the coverage range may be optimized. According to various aspectsof the present disclosure, the device ranges 114B may overlap and arenot limited to a configuration of ranges such as the ranges shown in thepresent embodiment.

Referring now to FIG. 4, an exemplary system architecture 400 is shown,according to one aspect of the present disclosure. In the presentembodiment, the central system and sensors of the UAVTMS 102 areillustrated sharing a connection over a network 108. In greater detail,the sensors of the UAVTMS 102 are represented as individual devices402A-402 n including various numbers of sensors. As previously describedin FIG. 1, a plurality of sensors (e.g., Wi-Fi, video, audio, RF, etc.)may be combined into an all-encompassing device 112, indicated in FIG. 4as 402A-402 n. The plurality of devices 402A-402 n, each potentiallyconfigured to include a certain number of different sensors, maytransmit sensor readings to the central system of the UAVTMS 102. In oneembodiment, Device 1, indicated as 402A, includes three sensors labeledSensor 1A, Sensor 1B, and Sensor 1C. It should be understood from thediscussion herein that Device 1, indicated as 402A, may include variousnumbers of sensors of various types (e.g., Wi-Fi, audio, video, RF,etc.). Device 2, indicated as 402B, includes four sensors labeled Sensor2A, Sensor 2B, Sensor 2C, and Sensor 2D. Device 1, indicated as 402A,may be substantially similar to Device 2, indicated as 402B, minus theone sensor that the two devices may not have in common, as shown in thepresent embodiment. In the present embodiment, a representation ofadditional devices, Device “n”, is included and indicated as 402 n. Insome embodiments, as many devices as necessary or appropriate may beconnected to the central system of the UAVTMS 102 over the network 108.In particular embodiments, the devices 402A-402 n are installed at aplurality of locations which may be remote or local to the centralsystem of the UAVTMS 102. Also operatively connected to the centralsystem of the UAVTMS 102 may be a plurality of computing devicescontrolled by a user 118 such as, mobile devices 418A, remote serversand systems 418B, and personal computers 418C, etc. As described in FIG.1, the computing devices controlled a user 118 may be connected to thecentral system of the UAVTMS 102 over a network 108 and may beconfigured to control or monitor the UAVTMS 102 and various locations ofthe deployed system and sensors, or analyze the information storedwithin the central system of the UAVTMS 102 by accessing a dashboard orportal. In some embodiments, computing devices such as third partydatabases 120 are connected to the central system of the UAVTMS 102 andmay be configured to operate autonomously.

Continuing with FIG. 4, an embodiment of the UAVTMS 102 is representedin greater detail than previously shown in FIG. 1. In the presentembodiment, the central system of the UAVTMS 102 includes the managementmodule 104, a Drone/UAV DNA database 412, a system management database414, and a web server 416 to be described below. In one embodiment, themanagement module 104 may be configured to intake the sensor informationfrom the devices 402A-402 n as transmitted over the network 108, thenprocess and analyze the information in order to determine how to respondto a detected UAV. In various embodiments, the sensor information fromdevices 402A-402 n may be processed locally at each device and then onlycertain results or values may be transmitted over the network 108 to themanagement module 104. In particular embodiments, the central system ofthe UAVTMS 102 may be local to the devices 402A-402 n. In theseparticular embodiments, the processing of the sensor information wouldbe performed locally which may eliminate the need to transmitinformation. In some embodiments, the data from the devices 402A-402 nmay be transmitted to the configuration module 404 represented in themanagement module 104. According to aspects of the present disclosure,the configuration module 404 may include the processes that interpretand analyze the data from the devices 402A-402 n in order to determineif a UAV is present. The data may then be further transmitted to themodule labeled aggregation 406. As will be discussed in greater detailin the discussion of FIG. 6, the aggregation module 406 may include theprocesses that combine the results and values, such as confidencelevels, from the configuration module 404 in order to determine if a UAVis detected. In the present embodiment, the two modules below theaggregation module 404 are labeled as “actions” 408 and “notifications”410. In various embodiments, these two modules may represent theprocesses that determine if a UAV has been identified and how to respondaccordingly. For example, processes operating within the aggregationmodule 406 may combine various confidence levels regarding UAVlikelihoods and determine that a UAV is present in a particular area.Further, the processes operating within the actions module 408 maydetermine that the UAV is an unrecognized UAV and a system moderatorshould be alerted. Continuing with the example, the processes operatingwithin the actions module 408 may forward the information regarding theidentified UAV to the notifications module 410 which may then send analert regarding the UAV to a user 118 of a user device.

In some scenarios, a particular sensor, such as a Wi-Fi sensor includedin the UAVTMS 102, may detect a UAV with 100% (or near 100%) confidence.In this scenario, the configuration module 404 may transmit theinformation regarding the detected UAV directly to the actions 408 ornotifications 410 modules without first transmitting information to theaggregation module 406 because the UAVTMS 102 has already established a100% (or near 100%) confidence and no further processing is required.

Included in the management module 104 and also connected to by abi-directional data path are the Drone/UAV DNA database 412 and systemmanagement database 414. These databases may include informationpertaining to the systems and methods performed within the managementmodule 104. The Drone/UAV DNA database 412 may include information thatallows the disclosed system to better identify and track UAVs. In oneembodiment, the Drone/UAV DNA database 412 may include meta-informationregarding UAVs either compiled over time by the UAVTMS 102 or madeavailable by UAV manufacturers, government agencies, or otherorganizations. This meta-information may be typical UAV weights,capabilities, and other technical specifications known about particularUAVs. In some embodiments, if a new UAV is detected by the system, themeta-information may be automatically uploaded to the Drone/UAV DNAdatabase 412 to include the new information corresponding to the newlydetected UAV. Similarly to the Drone/UAV DNA database 412, in variousembodiments the system management database 414 may include informationregarding UAV alerts, configurations, or other information regardinggeneral system diagnostics. In particular embodiments, the databasesincluded in the central system of the UAVTMS 102 may include anyappropriate information for UAV identification, tracking, and monitoringand should not be limited to the information discussed herein. Accordingto various aspects of the present disclosure, the databases included inthe UAVTMS 102 may be cloud based, virtual, local, or any otherappropriate form of computer memory.

Continuing with FIG. 4 and in one embodiment, the information stored inthe databases 412 and 414, as well as the information processed by themanagement module 104 may be accessible through a web server 416. Theweb server 416 may include a bi-directional link between the managementmodule 104, as well as bi-directional links between the at least onedatabase included in the central system of the UAVTMS 102. The webserver 416 may also include a bi-directional link and be operativelyconnected over the network 108 to the plurality of computing devices. Inthe present embodiment, the plurality of computing devices are indicatedas 418A, 418B, and 418C. In the present embodiment, 418A, 418B, and 418Cmay connect directly to the web server 416 included within the UAVTMS102. According to aspects of the present disclosure, the web server 416may allow for the plurality of computing devices 418A, 418B, and 418C toaccess the data included in the UAVTMS 102. In certain embodiments, itmay be useful for the computing devices 418A, 418B and 418C to haveaccess to the web server 416 because the web server 416 may allow theinformation processed and stored within the UAVTMS 102 to be shared withthe users 118 and monitors of the system. In one embodiment, the webserver 416 may allow for the plurality of computing devices 418A, 418B,and 418C to access live feeds from sensors. Shown in the presentembodiment, the web server 416 includes bi-directional links to all ofthe elements within the UAVTMS 102. In some embodiments, the web server416 may handle the querying of information from the UAVTMS 102 andtransmitting the queried information to the plurality of computingdevices 418A, 418B, and 418C. However, it should be understood from thediscussion herein that the computing devices shown in the presentembodiment are not intended to limit the scope of the disclosure, ratherthey are intended to portray the various possible computing devicescapable of communicating with the exemplary system.

As will be understood by one of ordinary skill in the art, the system,architectural components, and operative connections/communicationpathways shown in these figures are intended to be exemplary only. Invarious embodiments, the architectural components of the systems andmethods described herein may be distributed architectures (even thoughshown as a single component). In particular embodiments, thearchitectural components may be operatively connected in any suitableway.

According to one embodiment of the present disclosure, FIG. 5A is anexemplary sensor device 112, and FIG. 5B is an exemplary RF sensordevice 510. Together, and in various embodiments, FIGS. 5A and 5B areexemplary hardware devices including the plurality of sensors, asdescribed herein. In certain embodiments, a plurality of sensors may beincluded in one all-encompassing device, such as device 112, or varioussensors can be standalone sensors, such as the RF sensor device 510.Although two examples of sensor devices are shown, in variousembodiments it is possible to include all sensors in a single device.

Referring to FIG. 5A, a plurality of sensors are included in the device112 shown. The device 112 as shown in the present embodiment includes anX-shape with a circular center but it should be understood from thediscussion herein that the device 112, and the RF sensor device 510, mayhave any shape and are not limited to the shapes as shown on FIG. 5.According to certain aspects of the present disclosure, the armsprotruding from the circular center of the device 112 may house theincluded sensors. In one embodiment, the arms may be detachable andinterchangeable so as to configure the sensor device 112 with an optimalnumber of each sensor. In other embodiments, the device 112 may includemore or less than four arms, or no arms, in order to allow for variousconfigurations of sensors. In the present embodiment, a video sensor 502may be the circular center of the device 112. According to aspects ofthe present disclosure, the video sensor 502 may allow for the device112 to capture and maintain a video stream of a particular field ofview, as determined during configuration. In various embodiments, thevideo sensor 502 may capture 1080p HD resolution video and may beconfigurable within a 60-120 degree field of view, but also many otherfields of view depending on particular device configurations. In oneembodiment the video sensor 502 may also be capable of near infrared HDdetection. Generally, the video sensor 502 allows for the device 112 to“see” the particular object in order to classify it as a UAV or non-UAV.

The arms indicated as 504 in the present embodiment may be audio sensors504, according to aspects of the present disclosure. In certainembodiments, it may be desirable for a particular device to include morethan one sensor for reasons such as adding range, accuracy, consistency,or overall better coverage around a particular monitored area whendetecting UAVs. In the present embodiment, the device 112 includes twoaudio sensors 504. In various embodiments, the audio sensors 504 may becapable of detecting stereo audio, which includes audible sonic andultrasonic frequencies, ranging between 0-96 kHz, but it should beunderstood from the discussion herein that the audio sensors 504 may beconfigured to monitor any appropriate frequency range. Generally, theaudio sensor 504 allows for the device 112 to “hear” the particularobject in order to classify it as a UAV or non-UAV.

Continuing with FIG. 5A, the device 112 as shown in the presentembodiment includes at least one Wi-Fi sensor 506. In variousembodiments, UAVs may be connected over Wi-Fi to a wireless local areanetwork (WLAN). In one embodiment, including a Wi-Fi sensor 506 on thedevice 112 may allow for any UAV being controlled and/or being accessedover Wi-Fi to be detected. The process of detecting and analyzing Wi-Fisignals will be further discussed in greater detail in FIG. 13.

It should be understood from the discussion herein that any type ofappropriate sensor that could be useful in identifying, tracking, andmanaging UAVs may be included in the device 112, and this is indicatedat device arm 508 labeled “other”. In various embodiments, examples ofthese “other” sensors might include high-resolution thermal imagingsensors and radar sensors operating in the ISM-band (Ultra-Wide Band andmmWave-Radar) for detecting UAVs based on heat emissions or particularfrequency ranges. In certain embodiments, PTZ-Cameras (EO and Thermal)may be included in order to increase the range of video-basedidentification and tracking of UAVs. In certain embodiments, device 112and the attached sensor arms 502, 504, 506, and 508 may include ondevice computing capabilities and computer memory/storage in order toperform the various processes and functions described herein relating toidentifying, tracking, and managing UAVs.

Referring now to FIG. 5B, a single RF sensor device 510 is shown,according to one embodiment of the present disclosure. The RF sensordevice 510 may be a standalone sensor, as shown in the presentembodiment, or it may be included in the device 112. In one embodiment,the RF sensor may be capable of scanning various industrial, scientific,and medical (ISM) bands, as well as other frequency bands, and detectingsignals therein. In certain embodiments, the RF sensor may continuouslyscan and detect 5 GHz video signals, or signals on any other appropriatecarrier frequency and/or frequency range, and further decode the videosignals. According to aspects of the present disclosure, some UAVs areequipped with video cameras and may transmit the video signals back to abase station or computing system to be viewed by the UAVoperator/controller. In various embodiments, a base station may be aphysical remote-control, a smart phone, a video-receiver, or a similardevice. In one embodiment, these video signals transmitted from the UAVto a base station may provide information regarding the location of aUAV or the UAV controller, which may aide in the identifying andtracking of the UAV. In certain embodiments, the RF sensor device 510 isconfigured to detect these signals and extract any information from thesignal regarding the presence of a UAV. In some embodiments, the rangeof an RF sensor such as the RF sensor device 510 may extend to about 500meters; however, it should be understood from the discussion herein thatthe range of the RF sensor device 510 may vary according to variousconfigurations. It should be understood that the various sensorsdescribed herein are exemplary, and any type of sensor that may beuseful in identifying, tracking, and managing UAVs may be included inthe present system.

Turning now to FIG. 6, a flowchart is shown illustrating the exemplaryoverall system process 600, according to one embodiment of the presentdisclosure. As will be understood by one having ordinary skill in theart, the steps and processes shown in FIG. 6 (and those of all otherflowcharts and figures shown and described herein) may operateconcurrently and continuously, are generally asynchronous andindependent, and are not necessarily performed in the order shown.Generally, the processes may operate on an electronic computing device(e.g., laptop, remote server, sensor device etc.).

At a high level, and as will be described in further detail herein, FIG.6 illustrates the process of receiving data from or at sensors,processing the data, determining based on the processed data if a UAV ispresent, and then determining how to respond to the results. The firststep in this process occurs at step 602, where the system is initiallyconfigured. Step 602 may allow the system to adjust to and recognize thesurrounding environment. In one embodiment, a video sensor included inthe present system may need to establish a background before certainbackground subtraction algorithms can be performed. According to aspectsof the present disclosure, at step 602 the video sensor may capture acertain amount of frames in order to establish a background before thesystem can determine what are static or moving (UAV candidates) objectsin a frame. This configuration process of establishing a backgroundbefore the system begins to identify and track UAVs may be the firststep in the process as illustrated; however, a sensor may be configuredor reconfigured as necessary throughout the general flow of the systemprocesses. According to aspects of the present disclosure, step 602 mayinvolve configuring the system to adjust for the heading of the videosensor. In certain embodiments, when monitoring and tracking UAVs it maybe helpful to have recorded the heading (i.e., yaw, pitch, roll) of thevideo sensor capturing the data because knowing the heading, as well asthe location of the video sensor, may allow for the system to projectwhere the UAV is and will be physically located. In some embodiments,the audio and RF sensors may also be configured at step 602. In variousembodiments, it may be beneficial for the audio sensor to detect thefrequencies that may be common to the surrounding area. In oneembodiment, configuring the RF sensor at step 602 may includeestablishing a frequency range to monitor. In particular embodiments,the system configuration step 602 may include the physical installationand deployment of the device 112, various sensors, and other hardware onvarious installation locations 110. According to various aspects of thepresent disclosure, the step 602 is intended to allow for the system andincluded sensors to prepare for functional operation.

Continuing with step 602, configuration of the system may includeestablishing a connection between all sensors included in the UAVTMS102, devices 112, installation locations 110, networks 108, third partydata sources 120, etc. In various embodiments, exemplary operation ofthe system may rely on the ability of all elements of the system tocommunicate with each other.

Once the system is configured, each individual sensor may begin tocollect data and perform operations and processes on the collected dataas necessary. As shown in the present embodiment, the video dataanalysis process 700 (FIG. 7), audio data analysis process 1100 (FIG.11), RF data analysis process 1200 (FIG. 12), Wi-Fi analysis process1300 (FIG. 13), and other additional sensor acquisition and analysisprocesses 604 may begin to operate once the configuration of the systemat step 602 is complete. As will be understood by one having ordinaryskill in the art, these processes may operate autonomously,asynchronously, and independently of each other. In particularembodiments, these processes may or may not all operate at the sametime, and in some embodiments some processes might not operate at alldepending on the sensors included in the particular device 112.According to various aspects of the present disclosure, each processmentioned immediately above may begin once any prerequisiteconfigurations have been completed at step 602. For example, the Wi-Fidata analysis process 1300 may begin its exemplary process while thevideo data analysis process 700 is still being configured. In oneembodiment, these processes may operate locally and within the circuitryof each individual sensor. In other embodiments, the data from eachsensor may be directed to a separate computing element, such as themanagement module 104, for further processing. In general, each processmentioned above collects data and analyzes the data in order todetermine if a UAV is present in its respective surrounding area.Briefly mentioned above and in various embodiments, step 604 is anoptional process that may allow additional sensors to operate and besupported on the device 112. For example, if a sonar sensor was added tothe device 112, step 604 might incorporate the additional processesrequired to operate the sonar sensor in addition to the present systemconfiguration. In various embodiments, the sensor processes may output aconfidence level. This confidence level may be a number or value that isan indication of how likely it is that a detected object is a UAV.

At step 606, the confidence levels outputted from each sensor dataanalysis process may be aggregated. In one embodiment, the aggregationof confidence levels may include the use of mathematical processes todetermine if an identified object is a UAV or not. In variousembodiments, a scenario may arise where the confidence level based onthe data from a Wi-Fi sensor may indicate a near 100% UAV presence. Inthe same scenario, the confidence level based on the data from an audiosensor may indicate only a near 60% UAV presence. The aggregation step606 may be configured to take the confidence levels from each sensor andcombine them in order to make a decision regarding the presence of aUAV. In certain embodiments, the aggregation of confidence levels step606 may be optional if a particular sensor detects a near 100%confidence level and directly forwards the information to thenotifications module 410.

Proceeding to step 608, the system may evaluate if a UAV was identifiedbased on the confidence levels aggregated at step 606. In oneembodiment, evaluating if a UAV was identified based on a collection ofone or more confidence levels may include comparing the one or moreconfidence levels to a predetermined threshold of confidence levelsand/or rules regarding UAV likelihoods. In certain embodiments, step 608may be configured to automatically determine that the object detected isa UAV based on a near 100% confidence level indication from Wi-Fi data,or data from another sensor. In other embodiments, step 608 may beconfigured to process a combination or average of all confidence levels,and then compare the results to a predetermined threshold. For example,if the threshold for an object to be considered a UAV is 80%, and theaverage of all confidence levels is 75%, the object may be determinednon-UAV regardless if some of the confidence levels were above the 80%mark. The output from step 608 is typically a boolean, indicating a yesor no decision if a UAV has been identified. If it is determined that aUAV is identified, the process may continue to step 610 where theinformation regarding the UAV is stored for future use. According tovarious aspects of the present disclosure, step 610 may involveindicating within the system that if future detected signals indicatesubstantially similar characteristics to the signals previouslydetermined to be originating from a UAV, then those signals should beassumed to be from a UAV.

Continuing with FIG. 6, at step 610 the information regarding a detectedUAV is stored in a database such as the Drone/UAV DNA database 412 orsystem management database 414. In various embodiments, storing theinformation regarding the detected UAV includes storing any videos ofthe UAV, noise patterns, controlling frequencies, Wi-Fi networkspecifications, transmitted data such as video captures and GPSlocations, etc., that may be used in the future for detecting otherUAVs, or managing and recognizing previously detected UAVs.

At step 612, an action regarding the UAV may be made based on apredetermined rule set. This decision could include a wide variety ofactions, such as ignore the UAV because it is known to be trusted, orattempt to locate the controller of the UAV because it is identified asa threat. In one embodiment, these rules and instructions are stored inthe database 106 included in the UAVTMS 102. In some embodiments, thepredetermined rules may be categorized by confidence level thresholds orparticular types of sensors, such as a Wi-Fi sensor, that mayeffectively indicate a UAV presence. After a decision is made regardinghow to respond to the identified UAV at step 612, or if a UAV was notidentified at step 608, the system may determine if it should continuemonitoring the surroundings for UAVs at step 614. If the system is tocontinue monitoring, the process may proceed to the beginning of theflow where it will continue to intake sensor data. If at step 614 it isdetermined that the system should no longer continue monitoring, thenthe process may be terminated. In most scenarios, the system willmonitor continuously while operational. If it were decided todiscontinue monitoring at step 614, the system may need to reconfigurethe sensors and devices to transmit data to the management module 104.

FIG. 7 is a flowchart illustrating the exemplary video data analysisprocess 700, according to one embodiment of the present disclosure. Atstep 702, an individual video frame is received from the video datastream as recorded from the video sensor. In various embodiments, thevideo is recorded in a 1080p HD format, or another appropriate videoformat. In certain embodiments, the video stream may be transmitted tothe central system of the UAVTMS 102 over a network 108 for processing,or the video stream may be processed locally.

Proceeding to step 704, background subtraction algorithms may beperformed on the received video frame in order to detect changes betweenthe current video frame and a background frame. In general, videosensors operate by capturing multiple frames per second. Each frame fromthe video data stream may not seem to be significantly different fromthe previous frame; however, if the two frames are closely analyzed thendifferences between the frames can be found. As will be understood andappreciated by those skilled in the art, background subtraction is acomputing process whereby given a frame that represents a relativelyconstant background, motion or new elements in subsequent frames can bedetected by comparing the pixels and/or pixel color values of thedeclared background frame to new frames. In various embodiments, eachframe included in the video data stream is compared to the backgroundframe. In one embodiment, frames included in the video data stream areperiodically compared to the background frame, such as every fifth frameor another appropriate number of frames. After comparing the frames, anarea in the frame where the pixel values have changed is an indicationof motion or a delta between the frames. Generally, a typical backgroundsubtraction output may include only black and white pixels. In theseoutputs, a black pixel is swapped with the original pixels where therewas little or no delta detected, and a white pixel is swapped with theoriginal pixels where a delta between the pixel values were detected.For example, consider a video frame of a clear sky compared to a videoframe of a clear sky with a bird in the frame. According to theprinciples of background subtraction, subtracting corresponding pixelcolor values between the two frames may result in a difference at thelocation where the bird is present, and the output may be a backgroundof black pixels with a cluster of white pixels representing the bird. Inparticular embodiments, the background frame may be established in thesystem configuration step 602 previously described in FIG. 6. In oneembodiment, establishing a background may involve capturing only thefirst frame when a video sensor is configured. In other embodiments, abackground is established once there is minimal or no change between agroup of subsequent frames or for a certain amount of time. In certainembodiments, a background is continuously updated once established, andmay adjust for natural changes in the environment (e.g., cloudformations, natural lighting due to time of day, etc.).

At step 706, the output of the background subtraction algorithm isanalyzed to identify if regions of interest (ROIs) are present in theframe. In various embodiments, a region of interest is a cluster ofpixels in a video frame that may indicate a potential UAV and should befurther analyzed. In one embodiment, if movement was detected during thebackground subtraction process at step 704, that area of change may beidentified as a region of interest. In certain embodiments, if a clusterof pixels indicating movement is identified within a frame, the systemmay recognize a rectangular portion of the frame that fully encompassesthe cluster of pixels as a region of interest. As shown in FIG. 8, theregions of interest 802A-802C encompass certain objects within a framethat have been detected as moving. According to various aspects of thepresent disclosure, when a ROI is being processed or analyzed, thelocation of the ROI within the video frame may be located within theframe by referencing the top left corner pixel location and bottom rightcorner pixel location of the rectangular portion of the ROI in the videoframe as mentioned above.

In some embodiments, during the video data analysis process, an entirevideo frame may be processed between steps; however, the top left andbottom right corners of the rectangular portion of the video frame mayindicate the boundary of the ROI and limit the processing to just thatregion of pixels. If no ROIs are detected in the frame, then the processmay jump to step 720 to determine if there are more frames to beanalyzed. In one embodiment, if no ROIs are detected in the frames thenthe video frame received at step 702 may be substantially similar to thebackground frame. If it is determined at step 706 that ROIs are presentin the frame, the video data is forwarded to the ROI Tracking process1400.

As will be later described in FIG. 14, the ROI tracking process 1400involves comparing ROIs from the frame received at step 702 to ROIs toprevious frames stored in a database within the UAVTMS 102. In oneembodiment, comparing various aspects of ROIs such as size and positionwithin the frame may allow for the system to track ROIs and predictwhere they will be located over time. In certain embodiments, ROIs maybe rejected as possible UAVs during the ROI Tracking process 1400 basedon the results from the size and position comparisons mentioned above.According to aspects of the present disclosure, the output from the ROITracking process 1400 is one or more ROIs that have either been trackedor rejected. These ROIs further are directed to step 708 where it isdetermined if the ROI is tracked or rejected.

At step 708, the system determines if an ROI has been tracked. Asbriefly mentioned above, if an ROI has not been tracked then it has beenrejected as a possible UAV candidate during the ROI tracking process1400. In various embodiments, if an ROI has been rejected or nottracked, the process continues to step 720 where the system determinesif there are more frames to analyze. If an ROI has been tracked, thenthe system proceeds to step 710 where it is determined if the one ormore ROIs are in an attention region.

In one embodiment, step 710 determines if the one or more ROIs that werepreviously tracked in the ROI tracking process 1400 are in an attentionregion. According to various aspects of the present disclosure, anattention region is an area within a frame where heightened attentionshould be focused due to a recently UAV-classified ROI being presentwithin that area. In various embodiments, the purpose of an attentionregion is to prevent an ROI that may be a UAV from being falselyrejected during the scene learning process 900 operating within thesystem. Generally, an attention region is a circular region encompassinga certain area (proportional to the size of the ROI) of pixels withinthe frame where a recently classified UAV may be. In variousembodiments, the attention region may be of any shape appropriate andshould not be limited to being circular. A visual representation of aregion of interest and attention region can be seen in FIG. 8.

Continuing with FIG. 7, at step 710 if the one or more ROIs are includedin an attention region, the process may skip to the objectclassification process 1000. In this scenario, the process skips to theobject classification process 1000 because the region of interest ismost likely a UAV and may be labeled as a non-UAV during the scenelearning process 900 due to its location within an attention region. Invarious embodiments, if the one or more regions of interest are not inan attention region, the process proceeds to the scene learning process900. The scene learning process 900 will be discussed in greater detailin FIG. 9, but it should be understood that the scene learning process900 generally compares one or more regions of interest to various otherknow regions of interest in order to determine if the region of interestis new to a particular scene or if it is a reoccurring region ofinterest. In various embodiments, the output from the scene learningprocess 900 may be one or more regions of interest that are indicated aseither being recognized within the expected scene, or not.

At step 712, if the one or more regions of interest are not indicated asbeing recognized by the scene learning process 900 then the processproceeds to the object classification process 1000 in order to determineand classify what the regions of interest may be, and more particularlyif they are UAVs. If at step 712 it is determined that the one or moreregions of interest from the scene learning process 900 were recognizedwithin the scene, the process proceeds to step 714, where the one ormore regions of interest are stored for a finite period. In oneembodiment, storing the one or more regions of interest for a finiteperiod of time allows for the central system of the UAVTMS 102 to beable to compare the recently stored ROIs to newly detected ROIs.

As briefly mentioned above, ROIs that were indicated as not beingrecognized in the particular scene are forwarded to the objectclassification process 1000. Generally, the object classificationprocess 1000 compares various aspects of the one or more regions ofinterest to corresponding aspects of known UAVs and non-UAVs. In variousembodiments, the output from the object classification process 1000 isone or more regions of interest that have been classified as either UAVor non-UAV.

At step 716, the outcome from the object classification process 1000 isevaluated. If the one or more regions of interest were determined to benon-UAV, then the one or more regions of interest may be stored for afinite period of time within a database, such as the database 106 of thecentral system of the UAVTMS 102. If the one or more regions of interestwere classified as UAVs, then attention regions are added to the framesin the locations of the corresponding regions of interest. In variousembodiments, attention regions are added to frames as a result ofrecently identified and classified UAVs. At step 718, the attentionregions added to the frame correspond to the one or more regions ofinterest and may prevent the one or more regions of interest from beingdisqualified as UAVs during the scene learning process 900.

In one embodiment, at step 720 the process determines if there areadditional frames to be analyzed. In various embodiments, if there areadditional frames to be analyzed then the process may proceed to 702 inorder to receive an additional video frame. In certain embodiments, ifthere are no additional frames to analyze then the process mayterminate.

FIG. 8 is a representative frame 800 from a video stream at a particularvideo sensor, according to one embodiment of the present disclosure. Inthe present embodiment, a scene is shown including three identifiedregions of interest 802A-802C as well as one attention region 804. Inone embodiment, the three regions of interest 802A-802C may have beenidentified due to movement around these objects after the backgroundsubtraction algorithms were performed at step 704. The left most regionof interest is a set of trees 802A, the middle region of interest is abird 802B, and the right most region of interest is a UAV 802C. Invarious embodiments and as will be discussed later in the detaileddescription of FIG. 10, the region of interest identifying the set oftrees 802A may be analyzed and quickly dismissed as a non-UAV. Treesgenerally remain stationary over time; however, external factors such aswind may cause trees to bend and move slightly. This movement maytrigger trees to be flagged as regions of interest, but as will bediscussed further in the scene learning process 900, objects such astrees will most likely be quickly dismissed as non-UAVs. The centerregion of interest, the bird 802B, most likely was recognized by a videosensor and flagged as a region of interest once it flew into the videosensor's field of view. According to embodiments of the presentdisclosure, the bird 802B will most likely be dismissed as a non-UAV,but the bird 802B may require classification as compared to the trees802A. In many embodiments, a bird is not included in the scene-model. Insome embodiments, a video sensor may not be able to distinguish thedifference between a bird and a UAV and will assign the region ofinterest a higher confidence level. In other embodiments, the bird maybe assigned a low confidence level because the bird is common to thearea and has been recognized and classified as non-UAV many timesbefore. In particular embodiments, the objet classification process isconfigured to automatically negate the possibility of the bird 802Bbeing classified as a UAV without assigning a confidence level.

Continuing with FIG. 8, a region of interest including a UAV 802C ispresent in the representative frame 800. In one embodiment, the regionof interest including the UAV 802C may be initially classified as a UAV,as described later in the discussion of FIG. 10. In various embodiments,when a region of interest is classified as a UAV, an attention region804 is established around the region of interest 802C. In the presentembodiment, an attention region 804, indicated as a circle around theregion of interest including the UAV 802C, has been added to therepresentative frame 800. As mentioned previously in FIG. 7, anattention region 804 is a zone surrounding a recently classified UAVregion of interest, such as the region of interest including the UAV802C, intended to prevent the region of interest from potentially beingdisqualified as a non-UAV. In various embodiments, the attention region804 is proportional to the particular region of interest it correspondsto, such as the region of interest 802C, and may represent an areawithin the representative frame 800 where the region of interest 802Cmay reasonably be located after a certain amount of time. For example,in one scenario, the region of interest including a UAV 802C may beidentified in one location and be attributed an attention region 804,but then later the region of interest 802C may enter the region ofinterest including the trees 802A. In that scenario, the attentionregion 804 may prevent the scene learning process 900 from associatingthe region of interest including the UAV 802C as being included in theregion of interest of trees 802A. If the attention region 804 was notpresent, the region of interest including the UAV 802C may be mistakenfor the trees 802A, which may result in the inability to continuetracking the region of interest including the UAV 802C.

Still referring to FIG. 8, present in the representative frame 800 butnot identified as a region of interest is a house 806. The house 806 isnot labeled as a region of interest because it is a stationary objectand would not be indicated as having moved in a background subtractionalgorithm at step 704. According to various aspects of the presentdisclosure, the house 806 may be included as a part of the background.

Referring now to FIG. 9, a flowchart illustrating the scene learningprocess 900 is shown, according to one embodiment of the presentdisclosure. According to aspects of the present disclosure, the input tothe scene learning process 900 may be one or more ROIs not included inattention regions, received from the process described at step 710 ofFIG. 7. In general, the scene learning process 900 receives one or moreregions of interest not included in attention regions and compares theone or more regions of interest to various aspects of regions ofinterest that have been previously identified by the system.

In one embodiment, the scene learning process 900 begins at step 902 byanalyzing one or more regions of interest. As briefly mentioned above,these regions of interest were determined to not be included inattention regions at step 710.

In various embodiments, step 904 compares aspects and characteristics ofthe one or more regions of interest to corresponding aspects andcharacteristics of stored regions of interest. In various embodiments,the aspects and characteristics that may be compared include positionwithin the video frame, size of the region of interest, motioncharacteristics, content of the one or more regions of interest, andothers. In particular embodiments, the results from these comparisonsmay be evaluated based on predetermined thresholds, or the comparisonsmay need to be complete matches. In an example scenario, if a region ofinterest is compared to a recently stored region of interest and it isdetermined that both regions of interest are located in similarpositions within their respective video frames, the sizes of bothregions of interest are generally the same but one may have increased ordecreased slightly, both regions of interest have been tracked movingsimilar amounts and directions between frames, and if both regions ofinterest have similar content, it may be determined that the particularregion of interest received is recognized by the scene learning process900.

According to various aspects of the present disclosure, step 906determines if the one or more regions of interest received at step 902match any or all of the corresponding aspects and characteristicscompared during step 904. If at step 906 the results from the comparisonat step 904 match or are within a certain threshold, or if thecomparisons match completely, the process may proceed to step 908 wherethe one or more regions of interest are indicated as being includedwithin the scene. In various embodiments, being included within thescene may mean the particular one or more regions of interest may berecognized or remembered by the system. Otherwise, the one or moreregions of interest may not be identified as being included within thescene at step 908 and instead the process may proceed to step 910 whereit is determined if there are more regions of interest to compare.

At step 908, the one or more regions of interest that were determined tobe matches to previously detected regions of interest within theparticular scene and based on a predetermined threshold are indicated asbeing included within the scene and may be further recognizable by thescene learning process 900. In various embodiments, being recognized bythe scene learning process 900 may result in one or more regions ofinterest not being processed by the object classification process 1000.In one embodiment, once the one or more regions of interest areindicated as being included in the particular scene, the process mayproceed to step 910 where it is determined if there are more regions ofinterest to analyze. If there are more regions of interest to analyze,the process may proceed to step 902, otherwise the process mayterminate.

FIG. 10 is a flowchart illustrating the object classification process1000, according to one embodiment of the present disclosure. Accordingto aspects of the present disclosure, the object classification process1000 may be initiated when an unrecognized region of interest isidentified by the video sensor. In various embodiments the process maybegin by receiving one or more unrecognized regions of interest at step1002. The one or more regions of interest received at step 1002 may havebeen previously analyzed in the scene learning process 900 describedabove in FIG. 9, and the system may have determined that the one or moreregions of interest do not correspond with any previously classified orstored regions of interest. Once the one or more regions of interest arereceived at step 1002, the process may proceed to steps 1004A, 1004B,1004C, and 1004D, where various aspects of the one or more regions ofinterest are analyzed. In general, the steps 1004A, 1004B, 1004C, and1004D involve extracting meta-information from the one or more regionsof interest and each process may execute simultaneously but alsoindependently of each other.

In one embodiment, step 1004A involves extracting red, green, and blue(RGB) color values from the pixels within the one or more ROI locations.In various embodiments the ROI location may be determined from the topleft and bottom right locations of the rectangular portion of the videoframe forming the ROI boundary, as discussed above in FIG. 7. In certainembodiments, extracting RGB color values from the pixels included in theone or more ROIs may help determine if the ROI should be classified asUAV or non-UAV. In various embodiments, UAVs may typically bemanufactured out of similar materials, painted with similar colors, orhave similar shapes. In one embodiment, a recognizable pattern ofmaterial, paint colors, or shapes across various UAVs may allow for thesystem to classify a region of interest as a UAV based on the color andcolor gradient similarities.

In other embodiments, step 1004B may involve extracting the backgroundsubtraction data from the one or more regions of interest. In variousembodiments, the background subtraction data may be an indication of theshape of the moving object (as compared to the rigid shape of therectangular ROI) and how a region of interest is moving across aparticular frame. In one embodiment, the background subtraction data mayindicate a large delta between frames, which may indicate that theregion of interest is moving relatively fast or that the region ofinterest may be relatively close. In other embodiments, the backgroundsubtraction data may indicate a small delta between frames, which mayindicate that the region of interest is moving slowly or possibly thatthe region of interest is far away. This background subtractionmeta-data, in various embodiments, may allow for the objectclassification process 1000 to determine if a region of interest isbehaving in a manner which would be similar to that of a UAV and if theobject within the region of interest has a similar shape to that of aUAV.

Continuing with FIG. 10, step 1004C involves extracting RGB color valuesfrom the pixels within the one or more ROI locations from previousframes. In one embodiment, extracting RGB colors from ROIs of previousframes may allow for the object classification process 1000 to usetemporal color information to compare previously classified ROIs tocurrently unclassified ROIs in an attempt to make a similarclassification decision. In various embodiments, step 1004D may allowfor other ROI meta-information to be extracted from the current one ormore regions of interest, as well as regions of interest from otherframes.

In some embodiments, the video sensor may capture frames of differentsizes or dimensions than other video sensors. In order to ensureconsistency during the object classification process 1000, the framesmay be scaled to a uniform size at step 1006. Step 1006 is an optionalstep and need not always be executed. If the video frame does not needto be scaled, the process may continue to step 1008, where the one ormore regions of interest are compared to examples of known UAVs andnon-UAVs stored in databases 412 and 414. In one embodiment, the machinelearning technique referred to as deep learning may be used at step 1008to make comparisons between the one or more regions of interest and theexamples of known UAVs and non-UAVs. According to various aspects of thepresent disclosure, deep learning may allow for conclusions to be drawnfrom making relationships between many data points (sensor data). Deeplearning may allow for the system to not only better classify the one ormore regions of interest, but also improve the scope and efficiency ofthe current deep learning algorithms over time. In general, the systemmay become more sophisticated as the number of classified regions ofinterest increases because there will be more classified regions ofinterest to make comparisons and relationships to.

In one embodiment, the system proceeds to step 1010, where the UAV andnon-UAV confidence levels may be determined for the one or more regionsof interest. In various embodiments, if the comparison results from step1008 above indicate that previously UAV-classified regions of interestare substantially similar to the one or more regions of interestcurrently being analyzed then the UAV confidence level for the one ormore regions of interest may be relatively high. In other embodiments,the opposite of the above scenario may be true if the comparison resultsfrom the step 1008 above indicate that previously non-UAV classifiedregions of interest are substantially similar to the one or more regionsof interest currently being analyzed.

In various embodiments, the UAV and non-UAV confidence levels determinedat step 1010 are compared at step 1012. According to aspects of thepresent disclosure, if the UAV confidence level is greater than thenon-UAV confidence level then the process may proceed to step 1014 wherethe one or more regions of interest are classified as UAV. If it isdetermined that the UAV confidence level is not greater than the non-UAVconfidence level, then the process may to proceed to step 1016 where theone or more regions of interest are classified as non-UAV. In variousembodiments, once one or more regions of interest are classified as aUAV or non-UAV the one or more regions of interest may be stored in thedatabases 412 and 414 for future processes.

FIG. 11 is a flowchart illustrating the audio data analysis process1100, according to one embodiment of the present disclosure. In variousembodiments, the exemplary audio data analysis process 1100 shows how anaudio signal is analyzed and processed in order to determine if thesignal was produced by a UAV. At step 1102, the process 1100 begins whenan audio sensor receives or detects an audio signal, or when the centralsystem of the UAVTMS 102 receives an audio data from an audio sensor.According to aspects of the present disclosure, this signal may be ananalog or digital signal. In some scenarios, if an environment is knownto produce certain frequency patterns, then it is possible to detectfrequencies that are not common. In accordance with aspects of thepresent disclosure, UAVs may emit frequencies that are easilyrecognizable. In one embodiment, the audio signal may be emitted fromthe motors, propeller blades, or other components of a UAV.

Proceeding to step 1104, the audio signal may be converted from the timedomain (as received) to the frequency domain using a Fast FourierTransform (FFT), or another signal processing method. According toaspects of the present disclosure, an FFT may allow for a signal such asan audio signal to be represented as a combination of frequencies.Generally, an audio signal may include many frequencies but all that isheard to the human ear is one continuous noise. An FFT may allow foreach individual frequency included in the signal to be represented alonga frequency axis. In certain embodiments, converting the audio signal tothe frequency domain may involve converting the signal into 8096different values, each covering some portion of the audio frequenciesfrom 0 Hz to 192 kHz. According to various aspects of the presentdisclosure, the signal may be converted into more or less values asnecessary, and also the values may cover frequencies that exceed 192kHz.

In various embodiments, once the audio signal is converted to thefrequency domain at step 1104, the process may proceed to step 1106where the sound data is updated. According to aspects of the presentdisclosure, updating the sound data may include storing the detectedfrequencies in a database such as the database 106 of the UAVTMS 102. Inparticular embodiments, updating the sound data may include updating alog of information within the UAVTMS 102 regarding particularfrequencies and how often they are detected by the audio sensor. Oncethe sound data is updated at step 1106, the process may proceed to step1108, where the frequency to noise volume ratio may be compared to apredefined threshold. In certain embodiments, step 1108 may involveevaluating all of the frequencies detected in the signal. In someembodiments, while evaluating all of the frequencies present in thesignal, if a frequency sample with very low amplitude, such as noise, isevaluated, the system may determine that the amplitude corresponding tothe particular frequency is below a certain threshold and discard thesample. In certain embodiments, a frequency sample with high amplitudemay be disregarded, such as a sampling artifact. In various embodiments,step 1108 may be intended to filter out frequency samples that are mostlikely not indicative of a UAV.

In the above scenarios, if a frequency sample with a particularamplitude is disregarded at step 1108 for not being within a certainthreshold, the process may proceed to step 1110. In certain embodiments,at step 1110 the system may check if there are more frequencies from theconverted signal to analyze and compare. If at step 1110 it is decidedthat there are more frequencies to compare then the process may jumpback to the beginning of step 1108 in order to compare another frequencyfrom the updated sound data.

Continuing with FIG. 11, and according to one embodiment, at step 1108if it is determined that particular frequency to noise volume ratios arewithin a certain threshold, the process may proceed to step 1112, wherethe frequency may be compared to other stored and known UAV frequencies.For example, at step 1112 the system may be configured to compare thecurrent frequencies to frequencies know to be emitted by specific UAVs(e.g., DJI Phantom 2 at 18 kHz, etc.). In other embodiments, the systemmay be configured at step 1112 to analyze a frequency and determine if asignal is encoded into the frequency. In some embodiments, detecting asignal encoded into a frequency may be an indication of a UAV that mayor may not have been previously encountered. According to various aspectof the present disclosure, the comparisons and evaluations made at step1112 may result in a confidence level being determined regarding asignal being an indication of a UAV or not. If at step 1112 it isdetermined that a particular frequency does not match with a known UAVfrequency or frequency patter then the process may proceed to step 1110as briefly described above, otherwise the system may proceed to step1114.

In various embodiments, step 1114 includes taking predetermined actionregarding a recently detected UAV. In one embodiment, a recentlydetected audio signal may have included frequencies that are strongindications of a UAV and as a result, the system may be configured torespond accordingly. At step 1114, it may be decided to trigger an alarmregarding the potential UAV to be sent to a user 118 monitoring thesystem. In other embodiments, it may be decided at step 1114 to attemptto locate the potential UAV or the UAV controller in order to and takeforceful action such as jamming any UAV capabilities or possiblytargeting the UAV with a weapon (e.g., laser, missile, variousprojectiles, etc.). In particular embodiments, the predetermined actionmay simply comprise assigning a confidence level corresponding to theparticular frequency or audio signal for use in subsequent decisionmaking or analysis. Regardless of the outcome at step 1114, the processmay proceed to step 1116 where the data from previous steps istransmitted to a database or server, such as the database 106 of theUAVTMS 102. In other embodiments, the data from previous steps may bestored locally.

Still referring to FIG. 11, once the data from step 1116 is transmittedto a database or server such as the databases 106, 412, and 414, theprocess may proceed to step 1110. Briefly mentioned above, at step 1110it may be determined if there are more frequencies from the convertedsignal to compare. In one embodiment, if there are more frequencies tocompare then the process may jump back to the beginning of step 1108 inorder to compare another frequency from the updated sound data. Invarious embodiments, at step 1110 if it is determined that there are nomore frequencies from the converted signal to compare then the processmay proceed to step 1118.

In certain embodiments, at step 1118 the system may determine if theexemplary audio data analysis process 1100 should continue to receiveaudio signals or if it should terminate. According to various aspects ofthe present disclosure, if the system determines that it should continueto receive audio signals then the process may jump back to step 1102. Inmost scenarios, the system will continue to receive audio signals. If itwas decided to discontinue receiving audio signals at step 1118, theexemplary audio data analysis process 1100 may need to reconfigure theaudio sensor if the process were to later resume.

FIG. 12 is a flowchart illustrating the radio frequency analysis process1200, according to one embodiment of the present disclosure. UAVs arecommonly controlled from remote base stations that emit flight controlsto the UAVs over radio frequencies. In various embodiments, UAVs mayalso transmit data (e.g., live video captures, battery status, GPSlocation, etc.) back to the remote base stations. A UAV controller maytransmit RF signals to the UAV over any frequency included in the radiofrequency spectrum (3 kHz-300 GHz); however, RF communications aretypically restricted by government regulations to certain sub-rangeswithin the spectrum. Traditional radio transmitters and receivers aredesigned to operate under the frequency limitations as well asmodulation and channel coding capabilities as defined by their hardwareconfigurations. However, a software defined radio (SDR) is a device thatincludes hardware components that are reconfigurable by software,allowing the SDR to monitor a dynamic range of frequencies. A SDRgenerally operates similarly to a traditional radio, but thereconfigurable hardware in a SDR allows for it to monitor a larger rangeof frequencies without requiring additional hardware. In one embodiment,the system is configured with at least one SDR in order to monitor anenvironment for UAVs being controlled by signals communicated overvarious frequencies in the radio frequency spectrum.

In various embodiments, the exemplary RF data analysis process 1200begins at step 1202 when the RF sensor receiver is tuned to a particularfrequency. In one embodiment, the RF sensor includes a RF signalreceiver that is configurable to receive data from RF signals overcertain frequencies. According to aspects of the present disclosure,tuning the receiver at step 1202 may involve programming the SDR tomonitor particular frequencies. In various embodiments, step 1202 mayinvolve configuring the RF receiver to be tuned into a particularfrequency for a predetermined amount of time, and then re-tuning the RFreceiver to a different frequency after that predetermined amount oftime. In certain embodiments, this process is known as an “RF Sweep”.Once the RF sensor is tuned at step 1202, the RF receiver may detect asignal being carried over the particular tuned frequency, after whichthe process proceeds to step 1204.

According to various embodiments, the RF signal data is received at step1204. In one embodiment, receiving the RF signal data includes detectingvarious signal characteristics (e.g., signal power, energy, gain, etc.)that would not typically be present on an idle carrier frequency. Atstep 1204, receiving RF signal data including the various signalcharacteristics mentioned immediately above is an initial indicationthat a UAV being controlled by RF signal may be in the vicinity;however, the following steps allow for the system to further determinethe content of the received RF signal.

Indicated by the dashed box, steps 1206 and 1208 are optional in theexemplary RF data analysis process 1200. In one embodiment, step 1206includes filtering the received RF signal data, and step 1208 includesdetermining if the energy from the filtered RF signal data exceeds apredefined energy level. In various embodiments, filtering the RF signaldata at step 1206 may involve filtering out certain unwanted componentsfrom the RF signal data. According to various aspects of the presentdisclosure and as will be understood and appreciated by one skilled inthe art, certain filters such as low pass filters, band pass filters,high pass filters, etc., may allow for certain unwanted frequencies(such as noise or sampling artifacts) to be removed from the RF signaldata. Removing particular unwanted frequencies from the RF signal datamay allow for only a desired portion of the RF signal data to beanalyzed which may result in more accurate predictions regarding UAVpresence.

Once the RF signal data is filtered at step 1206, the system maydetermine if the signal energy is above a predefined threshold at step1208. In various embodiments, at step 1208 the circuitry within the RFsensor may be physically processing the received and filtered RF signaldata, and if the energy levels within the circuitry are not above apredefined threshold then the RF signal data may be rejected as a UAVidentifiable signal. Rejecting the RF signal data based on signal energybelow a predefined threshold may result in the process proceeding tostep 1218 in order to determine if the system should continue to receivemore RF signals. At step 1208, if it is determined that the energy fromthe RF signal data exceeds a certain predefined threshold then theprocess may proceed to step 1210.

In one embodiment, step 1210 involves filtering the RF signal data usinga Finite Impulse Response (FIR) Filter. Similarly to step 1206, step1210 filters the RF signal data and may remove particular frequenciesfrom the sampled RF signal data based on the particular filter design.In some embodiments, when steps 1206 and 1210 both occur in theexemplary RF data analysis process 1200, the filtering processes ofsteps 1206 and 1210 may operate in parallel and simultaneously filterthe same RF signal data.

Step 1212 of the exemplary RF data analysis process 1200 includes theprocess of decoding the RF signal data. In one embodiment, decoding theRF signal data allows for the message encoded into the RF signal to beextracted and further understood. In signal processing, the process ofencoding a message or information into an electromagnetic wave of acertain frequency is called modulation. In certain embodiments, oncethis signal data is received such as in step 1204, the message in thesignal cannot be understood until it is demodulated, or decoded. Thisprocess happens at step 1212 in the present embodiment. In variousembodiments, the signal decoded at step 1212 may include instructionsregarding future UAV behaviors, video captures from the UAV, UAV batterylevels, etc. Decoding the RF signal data at step 1212 may allow for acontroller of the system, such as a user 118, to extract and make use ofthe information detected in the particular RF signal.

At step 1214, the system may determine if the filtered and decoded RFsignal data is recognized by the system. In one embodiment, the filteredand decoded data may be a collection of frequencies and amplitudesrepresented along a frequency axis. According to various aspects of thepresent disclosure, this collection of frequencies may be arranged in aparticular pattern that may indicate a UAV presence. In someembodiments, the UAVTMS 102 may have RF frequency patterns known toindicate the controlling of a UAV from a remote base station stored indatabases such as databases 412 and 414 including calibration data orother data. The system may use these stored frequency patterns todetermine if the filtered and decoded RF signal data is recognized ornot. If at step 1214 it is determined that the frequency pattern isunrecognized, then the exemplary RF data analysis process 1200 maycontinue to step 1218 where it is determined if the system shouldcontinue to receive RF signals.

In one embodiment, if the frequency pattern is recognized at step 1214,the process may proceed to step 1216 where a predetermined rule setincluding information regarding how to respond to the recognizedfrequency pattern may determine how to best react to the recognized RFsignal data. In an example scenario, the monitored environment may be aresidential home and a recognized frequency pattern may be detected. Thesystem may then decide to alert a user, which may be the owner of thehouse in this scenario, to decide how to handle the situation. The usermay then decide simply to close the blinds in their home or potentiallycontact an authority. In certain embodiments, the predetermined rule setmay determine that a high confidence level should be assigned to theparticular RF signal data being received if the RF signal data isrecognized.

Once a decision is made based on a predetermined ruleset at step 1216,the exemplary process 1200 may proceed to step 1218 where the systemdetermines if it should continue receiving RF signals. In oneembodiment, if the system determines that it should continue to receiveRF signals, the process may jump to step 1202 in order to tune orre-tune the RF receiver. In other embodiments, if the system determinesthat it should not continue to receive RF signals then the process mayterminate.

FIG. 13 is a flowchart illustrating the exemplary Wi-Fi data analysisprocess 1300, according to one aspect of the present disclosure. Invarious embodiments, UAVs and other various remote controlled devicesmay be connected over Wi-Fi to a wireless local area network (WLAN) orestablish their own local Wi-Fi access points. Similar to connecting amobile device to the wireless internet at a local coffee shop, theprocess of connecting a UAV to a WLAN over Wi-Fi involves sharing someinitial information. Typically, and in various embodiments, thisinformation may include network names, passwords, device identifiers,and other relevant credentials. In one embodiment, a common credentialto share in order to gain network address is a MAC address. A MACaddress is a unique identifier assigned to devices or other networksinterfaces. In certain embodiments, when a device is connected to aWLAN, the MAC address of that device may be communicated to the networksource and included in any communications to and from the device.According to aspects of the present disclosure, a UAV may bemanufactured to include a unique MAC address, and a portion of that MACaddress may identify the manufacturer. In some embodiments, if a MACaddress is readable from communications being transmitted and receivedfrom a UAV, it may be possible to determine the UAV manufacturer whichmay provide insight regarding how to take action against the UAV ifnecessary.

In addition to MAC addresses, most Wi-Fi signals include a service setidentification (SSID) along with any other information being transmittedor received. In various embodiments, SSID's may be thought of as a“network name” as they are often used as network identifiers whentransmitting information. In various embodiments, once a device isconnected to a network, the SSID of that network will be included in anycommunication transmitted or received between the device and networkconnection source. In one embodiment, if a SSID is readable fromcommunications being transmitted over a network, it is possible todetermine if that network is trusted based on if the network SSID isrecognized.

In one embodiment, when communication signals such as Wi-Fi signalsreach a sensor they may be received with a particular strength. Thisstrength may be converted, through the internal circuitry of thereceiver, into a relative received signal strength indicator (RSSI). Incertain embodiments, a high RSSI may indicate a strong signal and a lowRSSI may indicate a weak signal. In various embodiments, this RSSI mayhelp determine how strong a network is and potentially how close a UAVis if that UAV were being controlled over Wi-Fi. In certain embodiments,a general Wi-Fi sensor may include the functionality required to detectWi-Fi signals and the information communicated over Wi-Fi signals asdescribed above.

Now beginning at step 1302 in the present embodiment, the Wi-Fi sensoris tuned to a particular channel. Generally, Wi-Fi signals aretransmitted over certain channels defined by certain frequencies.Typically, these channels are classified by numbers (i.e. 1-14) and aredefined by frequencies increasing on a scale of 5 MHz per channel. Forexample, channel one operates on 2412 MHz, channel 2 operates on 2417MHz, channel 3 operates on 2422 MHz, and so on. In one embodiment, atstep 1302 the Wi-Fi sensor is tuned and configured to monitor thesechannels in an effort to detect W-Fi data transfers. In variousembodiments, the Wi-Fi sensor may only be tuned to one channel at atime. In particular embodiments, the Wi-Fi sensor may be tuned tovarious channels at a time. In certain embodiments, being tuned to acertain frequency channel may allow for the Wi-Fi sensor to monitor eachchannel for a certain amount of time in order to detect a transfer ofdata packets between a UAV and a Wi-Fi access point. In one embodiment,the Wi-Fi sensor does not need to connect to a particular Wi-Fi networkin order to receive data from Wi-Fi signals being transmitted over thatnetwork because the network is a shared medium. According to aspects ofthe present disclosure, the Wi-Fi sensor may be configured to “sniff”the Wi-Fi transfers operating on various channels and extractinformation (e.g., SSID, MAC address, etc.) from the transfers withoutconnecting to the network. In some embodiments, the Wi-Fi sensor mayextract information such as SSID and MAC address from encrypted signalsby locating the information within certain packets (management frames).

Proceeding to step 1304, particular Wi-Fi data may be received from aWi-Fi sensor based on certain embodiment configurations. In oneembodiment, the Wi-Fi data received may include at least the RSSI, MACaddress, and SSID. In other embodiments, the Wi-Fi data may not includeinformation such as SSID due to the network identification beingconcealed. Once the Wi-Fi data is received, the process may proceed tostep 1306, where the RSSI is used to estimate proximity. According toaspects of the present disclosure, RSSI may be an indication of howclose or far a device, such as a UAV, is to a particular signal source.The RSSI based on the particular signal may not be a definitive measureof location, but may be used as an indication of proximity.

At step 1308, the proximity estimated in step 1306 may be compared to anallowable proximity threshold. If it is determined that the RSSI fromthe Wi-Fi data is past the allowable threshold, then further action mayneed to be pursued, as will be described below. It should be understoodfrom the discussion herein that the RSSI from a particular Wi-Fi signalmay be interpreted in various ways. In one embodiment, a RSSI may besubstantially low, indicating a weak signal. In some embodiments a weaksignal may be interpreted as a threat because it may suggest that thecontroller is located some distance away and is aware of the limits ofthe network. In other embodiments, a strong RSSI may indicate that thesource of the network is close by, and this may suggest that the networkwas recently established and potentially mobile, for example originatingfrom hardware stored in a vehicle or from a mobile phone hot spot.However, the exemplary Wi-Fi data analysis process 1300 may beconfigured to disregard Wi-Fi signals resulting in RSSI's that aredetermined at step 1308 to not be past the allowable threshold,resulting in the process to jump to step 1314. In one embodiment, if itis determined that the estimated proximity is past the allowablethreshold than the exemplary system 1300 proceeds to step 1310 where theMAC address and SSID information are analyzed both independently and asa combination.

In various embodiments, at step 1310 the MAC address from the receivedWi-Fi data may be compared to various known MAC addresses stored in adatabase within the system, such as the Drone/UAV DNA and systemmanagement databases 412 and 414. According to aspects of the presentdisclosure, MAC addresses from known UAV manufacturers may be stored ina database 106 within the UAVTMS 102 or in a third party database 120.Consider an example where the MAC addresses of UAVs known to bemanufactured from a popular UAV manufacturer such as 3D Robotics, Inc.,are stored in a database 106 within the UAVTMS 102 or in a third partydatabase 120. In certain embodiments, a UAV being controlled over Wi-Fimay be detected, and the MAC address may be compared the known storedMAC addresses. If the MAC addresses match or have fields of informationthat match the known MAC address information, this may indicate that thedetected Wi-Fi signal is controlling a 3D Robotics UAV. In scenarioslike the one described above, the exemplary process 1300 would proceedfrom step 1310 to step 1312 in order to make a decision based on apredetermined rule set. In one embodiment, if the MAC address fromreceived Wi-Fi data is a strong indication of a UAV then a predeterminedaction may be exercising forceful action against the UAV such as jammingUAV signals and communications or disabling the UAV with a laser orprojectile. In other embodiments, based on the MAC address of thedetected UAV, it may be preferred to attempt to locate the source of theUAV controller. In some embodiments, the predetermined ruleset at step1312 may assign a certain confidence level to the Wi-Fi data. If at step1310 the MAC address was not recognized, the exemplary process 1300 mayproceed to step 1314 where the system determines if it should continueto receive Wi-Fi signals.

Continuing with step 1310, analyzing the SSID may reveal the networkidentification. Similar to the storing of MAC addresses of known UAVsand UAV manufacturers, included in the database 106 of the UAVTMS 102,third party databases 120, or the Drone/UAV DNA and system managementdatabases 412 and 414, may be a list of known SSID's. In certainembodiments, the stored SSID's may be SSID's that are trusted, nottrusted, or a combination of both. According to aspects of the presentdisclosure, if at step 1310 it is determined that the SSID isrecognized, then the exemplary process 1300 may proceed to step 1312where a decision may be made according to a predetermined rule set. Inone embodiment, step 1310 may also analyze the MAC address and SSIDdetected in the Wi-Fi data as a combination. According to aspects of thepresent disclosure, if both the MAC address and SSID detected in Wi-Fidata are indicative of UAV activity, this may also result in the processproceeding to step 1312 in order to make a decision based on apredetermined ruleset. Similar to the description of step 1312 above,this decision based on a predetermined rule set may include variousactions such as forcefully engaging the UAV or attempting to locate theUAV controller. If at step 1310 is it determined that the SSID, MACaddress, or a combination of the two are not recognized, then theexemplary process 1300 may continue to step 1314 where it is determinedif the system should continue receiving Wi-Fi signals. In variousembodiments, if it is determined that the system should continue toreceive Wi-Fi signals, the exemplary process 1300 may proceed to step1302 where the system may tune the Wi-Fi sensor to a different channel.If at step 1314 it is determined that the system should not continue toreceive Wi-Fi signals, the exemplary process 1300 may terminate.

Turning now to FIG. 14, a flowchart is shown illustrating the exemplaryregion of interest tracking process 1400. In one embodiment, the processmay begin at step 1402 by receiving one or more regions of interest.Referring back to FIG. 7, step 706 determines if regions of interest areidentified in a video frame. If one or more regions of interest arepresent in the video frame than the one or more regions of interest maybe forwarded to the exemplary region of interest tracking process 1400.In general, tracking regions of interest allows for the system to beable to determine where regions of interest are and where they areheading based on their current behavior. Also, the region of interesttracking process 1400 may allow for the system to rule out, or reject,certain regions of interest as potential UAVs based on certainbehaviors. Once the exemplary region of interest tracking process 1400receives one or more regions of interest at step 1402, the process mayproceed to step 1404 where the process compares the sizes of the one ormore regions of interest to the sizes of the same regions of interest asthey were present in previous frames.

In various embodiments, the frame immediately previous to the currentframe may include substantially similar regions of interest to the oneor more regions of interest received at step 1402. In one embodiment, atstep 1404 the current one or more regions of interest may be compared tothe one or more previous regions of interest, and more specificallycompared to the sizes of the regions of interest. In certainembodiments, if one or more regions of interest increase or decrease insize by a certain amount, percentage, or any appropriate method ofmeasure, that increase or decrease may determine if the one or moreregions of interest may be classified as UAVs. At step 1406, the sizecomparisons from step 1404 are further compared to certain configuredthresholds. In some embodiments, the system may be configured toconsider a sudden or substantial increase in size of one or more regionsof interest uncharacteristic of a UAV, and then may determine that oneor more regions of interest that exhibit that type of behavior arenon-UAV. In other embodiments, the system may be configured to considernegligible or no change in size of one or more regions of interest alsouncharacteristic of UAVs. In certain embodiments, an appropriate amountof change for one or more regions of interest may be within a certainthreshold. If the amount of change of the one or more regions ofinterest is not within that threshold than the one or more regions ofinterest may be forwarded to and rejected at step 1408. In oneembodiment, rejecting one or more regions of interest at step 1408 mayinclude classifying the one or more regions of interest as non-UAV andfurther excluding the one or more regions of interest from furtherprocessing. If at step 1406 it is determined that the change in size ofthe one or more regions of interest is within an appropriate threshold,then the one or more regions of interest may be further compared toprevious frames at step 1410.

In one embodiment, step 1410 may include comparing the one or moreregions of interest currently being processed to the location of the oneor more substantially similar regions of interest from previous frames.In certain embodiments, comparing the one or more regions of interest tothe locations of one or more regions of interest from previous framesmay provide an indication of the speed of the one or more regions ofinterest. More particularly and according to various aspects of thepresent disclosure, the center positions of the one or more regions ofinterest may be compared to the centers of the one or more regions ofinterest from previous frames.

At step 1412, it may be determined if the comparison of the positions ofthe one or more regions of interest to the one or more regions ofinterest from previous frames are within a certain threshold. In someembodiments, the result from comparing the one or more regions ofinterest to previous regions of interest may be a difference in pixels,or a calculated theoretical distance based on the changed position ofthe one or more regions of interest within the frame. If at step 1412 itis determined that the center positions of the one or more regions ofinterest have moved a certain amount within a certain threshold, thenthe process may continue to step 1414 where the speed of the one or moreregions of interest are calculated. If at step 1412 it is determinedthat the center positions of the one or more regions of interest are notwithin an allowable threshold then the one or more regions of interestmay be forwarded to and rejected at step 1408.

In one embodiment, at step 1414 the speed of the one or more regions ofinterest can be calculated based on the degree of movement between theircurrent location within the frame and their locations in previousframes. In certain embodiments, calculating the speed of one or moreregions of interest may allow for the system to better estimate andanticipate where a potential UAV may be in the area being monitored.According to aspects of the present disclosure, the speed of the one ormore regions of interest, as well as the one or more regions of interestthemselves, may be stored in a database, such as the database 106 of theUAVTMS 102, to be used for future reference. Proceeding to step 1416, invarious embodiments, the one or more regions may be identified astracked regions of interest. Referring back to FIG. 7, if one or moreregions of interest have been identified as tracked then they mayproceed to the scene learning process 900. According to various aspectsof the present disclosure, identifying an object as tracked does notguarantee that it will further be classified as a UAV. In variousembodiments, identifying one or more regions of interest as tracked mayallow for the one or more regions of interest to be further processed inthe exemplary scene learning process 900. According to various aspectsof the present disclosure, the steps 1414 and 1416 involving calculatingthe speed of one or more ROIs and identifying one or more ROIs astracked may occur either simultaneously or interchangeably in order.

After either classifying one or more regions of interest as tracked atstep 1416 or rejecting one or more regions of interest as possible UAVsat step 1408, the last step of the exemplary region of interest trackingprocess 1400 is step 1418, where it may be determined if there are moreregions of interest to analyze. In various embodiments, if it isdetermined that there are additional regions of interest to analyze thenthe process may jump to step 1402 in order to receive one or more newregions of interest. If it is determined at step 1418 that there are noadditional or new regions of interest to analyze then the exemplaryregion of interest tracking process 1400 may terminate.

From the foregoing, it will be understood that various aspects of theprocesses described herein are software processes that execute oncomputer systems that form parts of the system. Accordingly, it will beunderstood that various embodiments of the system described herein aregenerally implemented as specially-configured computers includingvarious computer hardware components and, in many cases, significantadditional features as compared to conventional or known computers,processes, or the like, as discussed in greater detail herein.Embodiments within the scope of the present disclosure also includecomputer-readable media for carrying or having computer-executableinstructions or data structures stored thereon. Such computer-readablemedia can be any available media which can be accessed by a computer, ordownloadable through communication networks. By way of example, and notlimitation, such computer-readable media can comprise various forms ofdata storage devices or media such as RAM, ROM, flash memory, EEPROM,CD-ROM, DVD, or other optical disk storage, magnetic disk storage, solidstate drives (SSDs) or other data storage devices, any type of removablenonvolatile memories such as secure digital (SD), flash memory, memorystick, etc., or any other medium which can be used to carry or storecomputer program code in the form of computer-executable instructions ordata structures and which can be accessed by a computer.

When information is transferred or provided over a network or anothercommunications connection (either hardwired, wireless, or a combinationof hardwired or wireless) to a computer, the computer properly views theconnection as a computer-readable medium. Thus, any such a connection isproperly termed and considered a computer-readable medium. Combinationsof the above should also be included within the scope ofcomputer-readable media. Computer-executable instructions comprise, forexample, instructions and data which cause a computer to perform onespecific function or a group of functions.

Those skilled in the art will understand the features and aspects of asuitable computing environment in which aspects of the disclosure may beimplemented. Although not required, some of the embodiments of theclaimed inventions may be described in the context ofcomputer-executable instructions, such as program modules or engines, asdescribed earlier, being executed by computers in networkedenvironments. Such program modules are often reflected and illustratedby flow charts, sequence diagrams, exemplary screen displays, and othertechniques used by those skilled in the art to communicate how to makeand use such computer program modules. Generally, program modulesinclude routines, programs, functions, objects, components, datastructures, application programming interface (API) calls to othercomputers whether local or remote, etc. that perform particular tasks orimplement particular defined data types, within the computer.Computer-executable instructions, associated data structures and/orschemas, and program modules represent examples of the program code forexecuting steps of the methods disclosed herein. The particular sequenceof such executable instructions or associated data structures representexamples of corresponding acts for implementing the functions describedin such steps.

Those skilled in the art will also appreciate that the claimed and/ordescribed systems and methods may be practiced in network computingenvironments with many types of computer system configurations,including personal computers, smartphones, tablets, hand-held devices,multi-processor systems, microprocessor-based or programmable consumerelectronics, networked PCs, minicomputers, mainframe computers, and thelike. Embodiments of the claimed invention are practiced in distributedcomputing environments where tasks are performed by local and remoteprocessing devices that are linked (either by hardwired links, wirelesslinks, or by a combination of hardwired or wireless links) through acommunications network. In a distributed computing environment, programmodules may be located in both local and remote memory storage devices.

An exemplary system for implementing various aspects of the describedoperations, which is not illustrated, includes a computing deviceincluding a processing unit, a system memory, and a system bus thatcouples various system components including the system memory to theprocessing unit. The computer will typically include one or more datastorage devices for reading data from and writing data to. The datastorage devices provide nonvolatile storage of computer-executableinstructions, data structures, program modules, and other data for thecomputer.

Computer program code that implements the functionality described hereintypically comprises one or more program modules that may be stored on adata storage device. This program code, as is known to those skilled inthe art, usually includes an operating system, one or more applicationprograms, other program modules, and program data. A user may entercommands and information into the computer through keyboard, touchscreen, pointing device, a script containing computer program codewritten in a scripting language or other input devices (not shown), suchas a microphone, etc. These and other input devices are often connectedto the processing unit through known electrical, optical, or wirelessconnections.

The computer that affects many aspects of the described processes willtypically operate in a networked environment using logical connectionsto one or more remote computers or data sources, which are describedfurther below. Remote computers may be another personal computer, aserver, a router, a network PC, a peer device or other common networknode, and typically include many or all of the elements described aboverelative to the main computer system in which the inventions areembodied. The logical connections between computers include a local areanetwork (LAN), a wide area network (WAN), virtual networks (WAN or LAN),and wireless LANs (WLAN) that are presented here by way of example andnot limitation. Such networking environments are commonplace inoffice-wide or enterprise-wide computer networks, intranets, and theInternet.

When used in a LAN or WLAN networking environment, a computer systemimplementing aspects of the invention is connected to the local networkthrough a network interface or adapter. When used in a WAN or WLANnetworking environment, the computer may include a modem, a wirelesslink, or other mechanisms for establishing communications over the widearea network, such as the Internet. In a networked environment, programmodules depicted relative to the computer, or portions thereof, may bestored in a remote data storage device. It will be appreciated that thenetwork connections described or shown are exemplary and othermechanisms of establishing communications over wide area networks or theInternet may be used.

While various aspects have been described in the context of a preferredembodiment, additional aspects, features, and methodologies of theclaimed inventions will be readily discernible from the descriptionherein, by those of ordinary skill in the art. Many embodiments andadaptations of the disclosure and claimed inventions other than thoseherein described, as well as many variations, modifications, andequivalent arrangements and methodologies, will be apparent from orreasonably suggested by the disclosure and the foregoing descriptionthereof, without departing from the substance or scope of the claims.Furthermore, any sequence(s) and/or temporal order of steps of variousprocesses described and claimed herein are those considered to be thebest mode contemplated for carrying out the claimed inventions. Itshould also be understood that, although steps of various processes maybe shown and described as being in a preferred sequence or temporalorder, the steps of any such processes are not limited to being carriedout in any particular sequence or order, absent a specific indication ofsuch to achieve a particular intended result. In most cases, the stepsof such processes may be carried out in a variety of different sequencesand orders, while still falling within the scope of the claimedinventions. In addition, some steps may be carried out simultaneously,contemporaneously, or in synchronization with other steps.

The embodiments were chosen and described in order to explain theprinciples of the claimed inventions and their practical application soas to enable others skilled in the art to utilize the inventions andvarious embodiments and with various modifications as are suited to theparticular use contemplated. Alternative embodiments will becomeapparent to those skilled in the art to which the claimed inventionspertain without departing from their spirit and scope. Accordingly, thescope of the claimed inventions is defined by the appended claims ratherthan the foregoing description and the exemplary embodiments describedtherein.

What is claimed is:
 1. A method for identifying unmanned aerial vehicles(UAVs) in a particular air space, comprising the steps of: receivingvideo data from a particular video sensor proximate to the particularair space, the video data including at least one image of an object thatmay be a UAV flying within the particular air space; analyzing the videodata to determine a first confidence measure that the object in the atleast one image comprises a UAV; receiving audio signal data from aparticular audio sensor proximate to the particular air space, the audiosignal data including frequency data indicating a possible presence of aUAV within the particular air space; analyzing the audio signal data todetermine a second confidence measure that the frequency data comprisesa UAV; receiving radio frequency (RF) signal data from a particular RFsensor proximate to the particular air space, the RF signal dataincluding data indicating a possible presence of a UAV within theparticular air space; analyzing the RF signal data to determine a thirdconfidence measure that the RF signal data corresponds to a UAV;aggregating the first confidence measure, the second confidence measure,and the third confidence measure into a combined confidence measureindicating a possible presence of a UAV in the particular air space; andupon determination that the combined confidence measure exceeds apredetermined threshold value, storing an indication in a database thata UAV was identified in the particular air space.
 2. The method of claim1, wherein the step of analyzing the RF signal data to determine thethird confidence measure further comprises the steps of: filtering theRF signal data to remove one or more unwanted frequencies; decoding thefiltered RF signal data to generate a pattern of one or more frequenciesand one or more amplitudes representing the RF signal data; comparingthe pattern of the one or more frequencies and the one or moreamplitudes representing the RF signal data to known patterns offrequencies and amplitudes known to be associated with UAVs; and upondetermination that the pattern of the one or more frequencies and theone or more amplitudes representing the RF signal data substantiallymatches at least one of the known patterns, determining the thirdconfidence measure.
 3. The method of claim 1, further comprising thesteps of: receiving Wi-Fi signal data from a particular Wi-Fi sensorproximate to the particular air space, the Wi-Fi signal data includingdata indicating a possible presence of a UAV within the particular airspace; analyzing the Wi-Fi signal data to determine a fourth confidencemeasure that the Wi-Fi signal data corresponds to a UAV; and aggregatingthe fourth confidence measure into the combined confidence measure. 4.The method of claim 3, wherein the step of analyzing the Wi-Fi signaldata to determine the fourth confidence measure further comprises thesteps of: extracting a media access control (MAC) address from the Wi-Fisignal data; comparing the extracted MAC address to one or more knownMAC addresses known to be associated with UAVs; and upon determinationthat the extracted MAC address substantially matches at least one knownMAC address, determining the fourth confidence measure.
 5. The method ofclaim 3, wherein the step of analyzing the Wi-Fi signal data todetermine the fourth confidence measure further comprises the steps of:extracting a service set identifier (SSID) from the Wi-Fi signal data;comparing the extracted SSID to one or more known SSIDs known to beassociated with UAVs; and upon determination that the extracted SSIDsubstantially matches at least one known SSID, determining the fourthconfidence measure.
 6. The method of claim 3, wherein the step ofanalyzing the Wi-Fi signal data to determine the fourth confidencemeasure further comprises the steps of: extracting a received signalstrength indicator (RSSI) from the Wi-Fi signal data; and based on theextracted RSSI, estimating a physical distance of the object emanatingthe Wi-Fi signal data from the particular Wi-Fi sensor, whereby thephysical distance must be above a predetermined threshold distance valueto indicate the presence of a UAV.
 7. The method of claim 1, wherein thestep of analyzing the video data to determine a first confidence measurefurther comprises the steps of: identifying at least one region ofinterest (ROI) in at least one video frame in the video data, the atleast one ROI comprising the image of the object that may be a UAVflying within the particular air space; performing an objectclassification process with respect to the at least one ROI to determinewhether the object in the image is a UAV, the object classificationprocess comprising the steps of: extracting image data from the image ofthe at least one ROI; comparing the extracted image data to prior imagedata of objects known to be UAVs to determine a probability that theobject in the image is a UAV; and upon determination that theprobability that the object in the image is a UAV exceeds apredetermined threshold, determining the first confidence measure. 8.The method of claim 1, wherein the step of analyzing the audio data todetermine a second confidence measure further comprises the steps of:converting the audio signal data to frequency domain data such that theaudio signal data may be represented as one or more frequencies;determining if a frequency-to-noise volume for each of the one or morefrequencies is within a predetermined frequency-to-noise thresholdrange; upon determination that a respective frequency-to-noise volumefor a respective frequency of the converted audio signal data is withinthe predetermined frequency-to-noise threshold range, comparing therespective frequency to one or more UAV frequencies known to beassociated with UAVs; and upon determination that the respectivefrequency substantially matches at least one of the one or more UAVfrequencies known to be associated with UAVs, determining the secondconfidence measure.
 9. The method of claim 1, further comprising thestep of storing in the database the video data and audio signal data inassociation with the indication that the UAV was identified in theparticular air space.
 10. The method of claim 1, further comprising thestep of initiating an alert to a system user that a UAV has beendetected in the particular air space.
 11. The method of claim 1, whereinthe predetermined threshold value comprises a percentage.
 12. The methodof claim 1, wherein the particular video sensor and the particular audiosensor are enclosed in a unitary housing.
 13. A system for identifyingunmanned aerial vehicles (UAVs) in a particular air space, comprising: avideo sensor proximate to the particular air space, wherein the videosensor is configured to collect and transmit video data, the video dataincluding at least one image of an object that may be a UAV flyingwithin the particular air space; an audio sensor proximate to theparticular air space, wherein the audio sensor is configured to collectand transmit audio signal data, the audio signal data including at leastfrequency data indicating a possible presence of a UAV within theparticular air space; a radio frequency (RF) sensor proximate to theparticular air space, wherein the RF sensor is configured to collect RFsignal data, the RF signal data including at least data indicating apossible presence of a UAV within the particular air space; a database;and a processor operatively coupled to the video sensor, the audiosensor, the RF sensor, and the database, wherein the processor isoperative to: analyze the video data to determine a first confidencemeasure that the object in the at least one image comprises a UAV;analyze the audio signal data to determine a second confidence measurethat the frequency data comprises a UAV; analyze the RF signal data todetermine a third confidence measure that the RF signal data correspondsto a UAV; aggregate the first confidence measure, the second confidencemeasure, and the third confidence measure into a combined confidencemeasure indicating a possible presence of a UAV in the particular airspace; and upon determination that the combined confidence measureexceeds a predetermined threshold value, store an indication in thedatabase that a UAV was identified in the particular air space.
 14. Thesystem of claim 13, wherein the processor is further operative to:filter the RF signal data to remove one or more unwanted frequencies;decode the filtered RF signal to generate a pattern of one or morefrequencies and one or more amplitudes representing the RF signal data;compare the pattern of the one or more frequencies and the one or moreamplitudes representing the RF signal data to known patterns offrequencies and amplitudes known to be associated with UAVs; and upondetermination that the pattern of the one or more frequencies and theone or more amplitudes representing the RF signal data substantiallymatches at least one of the known patterns, determine the thirdconfidence measure.
 15. The system of claim 13, the system furthercomprising: a Wi-Fi sensor proximate to the particular air space,wherein the Wi-Fi sensor is configured to receive Wi-Fi signal data, theWi-Fi signal data including data indicating a possible presence of a UAVwithin the particular air space; and the processor further operativelycoupled to the Wi-Fi sensor, the processor further operative to: analyzethe Wi-Fi signal data to determine a fourth confidence measure that theWi-Fi signal data corresponds to a UAV; and aggregate the fourthconfidence measure into the combined confidence measure.
 16. The systemof claim 15, wherein the processor is further operative to: extract amedia access control (MAC) address from the Wi-Fi signal data; comparethe extracted MAC address to one or more known MAC addresses known to beassociated with UAVs; and; upon determination that the extracted MACaddress substantially matches at least one known MAC address, determinethe fourth confidence measure.
 17. The system of claim 15, wherein theprocessor is further operative to: extract a service set identifier(SSID) from the Wi-Fi signal data; compare the extracted SSID to one ormore known SSIDs known to be associated with UAVs; and upondetermination that the extracted SSID substantially matches at least oneknown SSID, determining the fourth confidence measure.
 18. The system ofclaim 15, wherein the processor is further operative to: extract areceived signal strength indicator (RSSI) from the Wi-Fi signal data;and based on the extracted RSSI, estimate a physical distance of theobject emanating the Wi-Fi signal data from the particular Wi-Fi sensor;whereby the physical distance must be above a predetermined thresholddistance value to indicate the presence of a UAV.
 19. The system ofclaim 13, wherein the processor is further operative to: identify atleast one region of interest (ROI) in at least one video frame in thevideo data, the at least one ROI comprising the image of the object thatmay be a UAV flying within the particular air space; and perform anobject classification process with respect to the at least one ROI todetermine whether the object in the image is a UAV, the objectclassification process comprising the steps of: extracting image datafrom the image of the at least one ROI; comparing the extracted imagedata to prior image data of objects known to be UAVs to determine aprobability that the object in the image is a UAV; and upondetermination that the probability that the object in the image is a UAVexceeds a predetermined threshold, determining the first confidencemeasure.
 20. The system of claim 13, wherein in addition to beingoperative to analyze the audio data to determine a second confidencemeasure, the processor is further operative to: convert the audio signaldata to frequency domain data such that the audio signal data may berepresented as one or more frequencies; determine if afrequency-to-noise volume for each of the one or more frequencies iswithin a predetermined frequency-to-noise volume threshold range; upondetermination that a respective frequency-to-noise volume for arespective frequency of the converted audio signal data is within thepredetermined frequency-to-noise threshold range, compare the respectivefrequency to one or more UAV frequencies known to be associated withUAVs; and upon determination that the respective frequency substantiallymatches at least one of the one or more UAV frequencies known to beassociated with UAVs, determine the second confidence measure.
 21. Thesystem of claim 13, wherein the processor is further operative to storein the database the video data and audio signal data in association withthe indication that the UAV was identified in the particular air space.22. The system of claim 13, wherein the processor is further operativeto alert a system user that a UAV has been detected in the particularair space.
 23. The system of claim 13, wherein the predeterminedthreshold value comprises a percentage.
 24. The system of claim 13,wherein the video and audio sensor are enclosed in a unitary housing.25. A method for identifying unmanned aerial vehicles (UAVs) in aparticular air space, comprising the steps of: receiving video data froma particular video sensor proximate to the particular air space, thevideo data including at least one image of an object that may be a UAVflying within the particular air space; analyzing the video data todetermine a first confidence measure that the object in the at least oneimage comprises a UAV; receiving audio signal data from a particularaudio sensor proximate to the particular air space, the audio signaldata including frequency data indicating a possible presence of a UAVwithin the particular air space; analyzing the audio signal data todetermine a second confidence measure that the frequency data comprisesa UAV; receiving Wi-Fi signal data from a particular Wi-Fi sensorproximate to the particular air space, the Wi-Fi signal data includingdata indicating a possible presence of a UAV within the particular airspace; analyzing the Wi-Fi signal data to determine a third confidencemeasure that the Wi-Fi signal data corresponds to a UAV; aggregating thefirst confidence measure, the second confidence measure, and the thirdconfidence measure into a combined confidence measure indicating apossible presence of a UAV in the particular air space; upondetermination that the combined confidence measure exceeds apredetermined threshold value, storing an indication in a database thata UAV was identified in the particular air space.
 26. The method ofclaim 25, further comprising the steps of: receiving radio frequency(RF) signal data from a particular RF sensor proximate to the particularair space, the RF signal data including data indicating a possiblepresence of a UAV within the particular air space; analyzing the RFsignal data to determine a fourth confidence measure that the RF signaldata corresponds to a UAV; and aggregating the fourth confidence measureinto the combined confidence measure.
 27. The method of claim 26,wherein the step of analyzing the RF signal data to determine the fourthconfidence measure further comprises the steps of: filtering the RFsignal data to remove one or more unwanted frequencies; decoding thefiltered RF signal data to generate a pattern of one or more frequenciesand one or more amplitudes representing the RF signal data; comparingthe pattern of the one or more frequencies and the one or moreamplitudes representing the RF signal data to known patterns offrequencies and amplitudes known to be associated with UAVs; and upondetermination that the pattern of the one or more frequencies and theone or more amplitudes representing the RF signal data substantiallymatches at least one of the known patterns, determining the fourthconfidence measure.
 28. The method of claim 25, wherein the step ofanalyzing the Wi-Fi signal data to determine the third confidencemeasure further comprises the steps of: extracting a media accesscontrol (MAC) address from the Wi-Fi signal data; comparing theextracted MAC address to one or more known MAC addresses known to beassociated with UAVs; and upon determination that the extracted MACaddress substantially matches at least one known MAC address,determining the third confidence measure.
 29. The method of claim 25,wherein the step of analyzing the Wi-Fi signal data to determine thethird confidence measure further comprises the steps of: extracting aservice set identifier (SSID) from the Wi-Fi signal data; comparing theextracted SSID to one or more known SSIDs known to be associated withUAVs; and upon determination that the extracted SSID substantiallymatches at least one known SSID, determining the third confidencemeasure.
 30. The method of claim 25, wherein the step of analyzing theWi-Fi signal data to determine the third confidence measure furthercomprises the steps of: extracting a received signal strength indicator(RSSI) from the Wi-Fi signal data; and based on the extracted RSSI,estimating a physical distance of the object emanating the Wi-Fi signaldata from the particular Wi-Fi sensor, whereby the physical distancemust be above a predetermined threshold distance value to indicate thepresence of a UAV.
 31. The method of claim 25, wherein the step ofanalyzing the video data to determine a first confidence measure furthercomprises the steps of: identifying at least one region of interest(ROI) in at least one video frame in the video data, the at least oneROI comprising the image of the object that may be a UAV flying withinthe particular air space; performing an object classification processwith respect to the at least one ROI to determine whether the object inthe image is a UAV, the object classification process comprising thesteps of: extracting image data from the image of the at least one ROI;comparing the extracted image data to prior image data of objects knownto be UAVs to determine a probability that the object in the image is aUAV; and upon determination that the probability that the object in theimage is a UAV exceeds a predetermined threshold, determining the firstconfidence measure.
 32. The method of claim 25, wherein the step ofanalyzing the audio data to determine a second confidence measurefurther comprises the steps of: converting the audio signal data tofrequency domain data such that the audio signal data may be representedas one or more frequencies; determining if a frequency-to-noise volumefor each of the one or more frequencies is within a predeterminedfrequency-to-noise threshold range; upon determination that a respectivefrequency-to-noise volume for a respective frequency of the convertedaudio signal data is within the predetermined frequency-to-noisethreshold range, comparing the respective frequency to one or more UAVfrequencies known to be associated with UAVs; and upon determinationthat the respective frequency substantially matches at least one of theone or more UAV frequencies known to be associated with UAVs,determining the second confidence measure.
 33. The method of claim 25,further comprising the step of storing in the database the video dataand audio signal data in association with the indication that the UAVwas identified in the particular air space.
 34. The method of claim 25,further comprising the step of initiating an alert to a system user thata UAV has been detected in the particular air space.
 35. The method ofclaim 25, wherein the predetermined threshold value comprises apercentage.
 36. The method of claim 25, wherein the particular videosensor and the particular audio sensor are enclosed in a unitaryhousing.
 37. A method for identifying unmanned aerial vehicles (UAVs) ina particular air space, comprising the steps of: receiving video datafrom a particular video sensor proximate to the particular air space,the video data including at least one image of an object that may be aUAV flying within the particular air space; analyzing the video data todetermine a first confidence measure that the object in the at least oneimage comprises a UAV; receiving audio signal data from a particularaudio sensor proximate to the particular air space, the audio signaldata including frequency data indicating a possible presence of a UAVwithin the particular air space; analyzing the audio signal data todetermine a second confidence measure that the frequency data comprisesa UAV, wherein analyzing the audio signal data comprises the steps of:converting the audio signal data to frequency domain data such that theaudio signal data may be represented as one or more frequencies;determining if a frequency-to-noise volume for each of the one or morefrequencies is within a predetermined frequency-to-noise thresholdrange; upon determination that a respective frequency-to-noise volumefor a respective frequency of the converted audio signal data is withinthe predetermined frequency-to-noise threshold range, comparing therespective frequency to one or more UAV frequencies known to beassociated with UAVs; and upon determination that the respectivefrequency substantially matches at least one of the one or more UAVfrequencies known to be associated with UAVs, determining the secondconfidence measure; aggregating the first confidence measure and thesecond confidence measure into a combined confidence measure indicatinga possible presence of a UAV in the particular air space; and upondetermination that the combined confidence measure exceeds apredetermined threshold value, storing an indication in a database thata UAV was identified in the particular air space.
 38. The method ofclaim 37, further comprising the steps of: receiving radio frequency(RF) signal data from a particular RF sensor proximate to the particularair space, the RF signal data including data indicating a possiblepresence of a UAV within the particular air space; analyzing the RFsignal data to determine a third confidence measure that the RF signaldata corresponds to a UAV; and aggregating the third confidence measureinto the combined confidence measure.
 39. The method of claim 38,wherein the step of analyzing the RF signal data to determine the thirdconfidence measure further comprises the steps of: filtering the RFsignal data to remove one or more unwanted frequencies; decoding thefiltered RF signal data to generate a pattern of one or more frequenciesand one or more amplitudes representing the RF signal data; comparingthe pattern of the one or more frequencies and the one or moreamplitudes representing the RF signal data to known patterns offrequencies and amplitudes known to be associated with UAVs; and upondetermination that the pattern of the one or more frequencies and theone or more amplitudes representing the RF signal data substantiallymatches at least one of the known patterns, determining the thirdconfidence measure.
 40. The method of claim 37, further comprising thesteps of: receiving Wi-Fi signal data from a particular Wi-Fi sensorproximate to the particular air space, the Wi-Fi signal data includingdata indicating a possible presence of a UAV within the particular airspace; analyzing the Wi-Fi signal data to determine a fourth confidencemeasure that the Wi-Fi signal data corresponds to a UAV; and aggregatingthe fourth confidence measure into the combined confidence measure. 41.The method of claim 40, wherein the step of analyzing the Wi-Fi signaldata to determine the fourth confidence measure further comprises thesteps of: extracting a media access control (MAC) address from the Wi-Fisignal data; comparing the extracted MAC address to one or more knownMAC addresses known to be associated with UAVs; and upon determinationthat the extracted MAC address substantially matches at least one knownMAC address, determining the fourth confidence measure.
 42. The methodof claim 40, wherein the step of analyzing the Wi-Fi signal data todetermine the fourth confidence measure further comprises the steps of:extracting a service set identifier (SSID) from the Wi-Fi signal data;comparing the extracted SSID to one or more known SSIDs known to beassociated with UAVs; and upon determination that the extracted SSIDsubstantially matches at least one known SSID, determining the fourthconfidence measure.
 43. The method of claim 40, wherein the step ofanalyzing the Wi-Fi signal data to determine the fourth confidencemeasure further comprises the steps of: extracting a received signalstrength indicator (RSSI) from the Wi-Fi signal data; and based on theextracted RSSI, estimating a physical distance of the object emanatingthe Wi-Fi signal data from the particular Wi-Fi sensor, whereby thephysical distance must be above a predetermined threshold distance valueto indicate the presence of a UAV.
 44. The method of claim 37, whereinthe step of analyzing the video data to determine a first confidencemeasure further comprises the steps of: identifying at least one regionof interest (ROI) in at least one video frame in the video data, the atleast one ROI comprising the image of the object that may be a UAVflying within the particular air space; performing an objectclassification process with respect to the at least one ROI to determinewhether the object in the image is a UAV, the object classificationprocess comprising the steps of: extracting image data from the image ofthe at least one ROI; comparing the extracted image data to prior imagedata of objects known to be UAVs to determine a probability that theobject in the image is a UAV; and upon determination that theprobability that the object in the image is a UAV exceeds apredetermined threshold, determining the first confidence measure. 45.The method of claim 37, further comprising the step of storing in thedatabase the video data and audio signal data in association with theindication that the UAV was identified in the particular air space. 46.The method of claim 37, further comprising the step of initiating analert to a system user that a UAV has been detected in the particularair space.
 47. The method of claim 37, wherein the predeterminedthreshold value comprises a percentage.
 48. The method of claim 37,wherein the particular video sensor and the particular audio sensor areenclosed in a unitary housing.
 49. A system for identifying unmannedaerial vehicles (UAVs) in a particular air space, comprising: a videosensor proximate to the particular air space, wherein the video sensoris configured to collect and transmit video data, the video dataincluding at least one image of an object that may be a UAV flyingwithin the particular air space; an audio sensor proximate to theparticular air space, wherein the audio sensor is configured to collectand transmit audio signal data, the audio signal data including at leastfrequency data indicating a possible presence of a UAV within theparticular air space; a Wi-Fi sensor proximate to the particular airspace, wherein the Wi-Fi sensor is configured to receive Wi-Fi signaldata, the Wi-Fi signal data including data indicating a possiblepresence of a UAV within the particular air space; a database; and aprocessor operatively coupled to the video sensor, the audio sensor, theWi-Fi sensor, and the database, wherein the processor is operative to:analyze the video data to determine a first confidence measure that theobject in the at least one image comprises a UAV; analyze the audiosignal data to determine a second confidence measure that the frequencydata comprises a UAV; analyze the Wi-Fi signal data to determine a thirdconfidence measure that the Wi-Fi signal data corresponds to a UAV;aggregate the first confidence measure, the second confidence measure,and the third confidence measure into a combined confidence measureindicating a possible presence of a UAV in the particular air space; andupon determination that the combined confidence measure exceeds apredetermined threshold value, store an indication in the database thata UAV was identified in the particular air space.
 50. The system ofclaim 49, further comprising: a radio frequency (RF) sensor proximate tothe particular air space, wherein the RF sensor is configured to collectRF signal data, the RF signal data including at least data indicating apossible presence of a UAV within the particular air space; theprocessor further operatively coupled to the RF sensor, wherein theprocessor is further operative to: analyze the RF signal data todetermine a fourth confidence measure that the RF signal datacorresponds to a UAV; and aggregate the fourth confidence measure intothe combined confidence measure.
 51. The system of claim 50, wherein theprocessor is further operative to: filter the RF signal data to removeone or more unwanted frequencies; decode the filtered RF signal togenerate a pattern of one or more frequencies and one or more amplitudesrepresenting the RF signal data; compare the pattern of the one or morefrequencies and the one or more amplitudes representing the RF signaldata to known patterns of frequencies and amplitudes known to beassociated with UAVs; and upon determination that the pattern of the oneor more frequencies and the one or more amplitudes representing the RFsignal data substantially matches at least one of the known patterns,determine the fourth confidence measure.
 52. The system of claim 49,wherein the processor is further operative to: extract a media accesscontrol (MAC) address from the Wi-Fi signal data; compare the extractedMAC address to one or more known MAC addresses known to be associatedwith UAVs; and; upon determination that the extracted MAC addresssubstantially matches at least one known MAC address, determine thethird confidence measure.
 53. The system of claim 49, wherein theprocessor is further operative to: extract a service set identifier(SSID) from the Wi-Fi signal data; compare the extracted SSID to one ormore known SSIDs known to be associated with UAVs; and upondetermination that the extracted SSID substantially matches at least oneknown SSID, determining the third confidence measure.
 54. The system ofclaim 49, wherein the processor is further operative to: extract areceived signal strength indicator (RSSI) from the Wi-Fi signal data;and based on the extracted RSSI, estimate a physical distance of theobject emanating the Wi-Fi signal data from the particular Wi-Fi sensor;whereby the physical distance must be above a predetermined thresholddistance value to indicate the presence of a UAV.
 55. The system ofclaim 49, wherein the processor is further operative to: identify atleast one region of interest (ROI) in at least one video frame in thevideo data, the at least one ROI comprising the image of the object thatmay be a UAV flying within the particular air space; and perform anobject classification process with respect to the at least one ROI todetermine whether the object in the image is a UAV, the objectclassification process comprising the steps of: extracting image datafrom the image of the at least one ROI; comparing the extracted imagedata to prior image data of objects known to be UAVs to determine aprobability that the object in the image is a UAV; and upondetermination that the probability that the object in the image is a UAVexceeds a predetermined threshold, determining the first confidencemeasure.
 56. The system of claim 49, wherein the processor is furtheroperative to: convert the audio signal data to frequency domain datasuch that the audio signal data may be represented as one or morefrequencies; determine if a frequency-to-noise volume for each of theone or more frequencies is within a predetermined frequency-to-noisevolume threshold range; upon determination that a respectivefrequency-to-noise volume for a respective frequency of the convertedaudio signal data is within the predetermined frequency-to-noisethreshold range, compare the respective frequency to one or more UAVfrequencies known to be associated with UAVs; and upon determinationthat the respective frequency substantially matches at least one of theone or more UAV frequencies known to be associated with UAVs, determinethe second confidence measure.
 57. The system of claim 49, wherein theprocessor is further operative to store in the database the video dataand audio signal data in association with the indication that the UAVwas identified in the particular air space.
 58. The system of claim 49,wherein the processor is further operative to alert a system user that aUAV has been detected in the particular air space.
 59. The system ofclaim 49, wherein the predetermined threshold value comprises apercentage.
 60. The system of claim 49, wherein the video and audiosensor are enclosed in a unitary housing.
 61. A system for identifyingunmanned aerial vehicles (UAVs) in a particular air space, comprising: avideo sensor proximate to the particular air space, wherein the videosensor is configured to collect and transmit video data, the video dataincluding at least one image of an object that may be a UAV flyingwithin the particular air space; an audio sensor proximate to theparticular air space, wherein the audio sensor is configured to collectand transmit audio signal data, the audio signal data including at leastfrequency data indicating a possible presence of a UAV within theparticular air space; a database; and a processor operatively coupled tothe video sensor, the audio sensor, and the database, wherein theprocessor is operative to: analyze the video data to determine a firstconfidence measure that the object in the at least one image comprises aUAV; analyze the audio signal data to determine a second confidencemeasure that the frequency data comprises a UAV, wherein analyzing theaudio signal data further comprises: converting the audio signal data tofrequency domain data such that the audio signal data may be representedas one or more frequencies; determining if a frequency-to-noise volumefor each of the one or more frequencies is within a predeterminedfrequency-to-noise volume threshold range; upon determination that arespective frequency-to-noise volume for a respective frequency of theconverted audio signal data is within the predeterminedfrequency-to-noise threshold range, comparing the respective frequencyto one or more UAV frequencies known to be associated with UAVs; andupon determination that the respective frequency substantially matchesat least one of the one or more UAV frequencies known to be associatedwith UAVs, determine the second confidence measure; aggregate the firstconfidence measure and the second confidence measure into a combinedconfidence measure indicating a possible presence of a UAV in theparticular air space; and upon determination that the combinedconfidence measure exceeds a predetermined threshold value, store anindication in the database that a UAV was identified in the particularair space.
 62. The system of claim 61, further comprising: a radiofrequency (RF) sensor proximate to the particular air space, wherein theRF sensor is configured to collect RF signal data, the RF signal dataincluding at least data indicating a possible presence of a UAV withinthe particular air space; the processor further operatively coupled tothe RF sensor, wherein the processor is further operative to: analyzethe RF signal data to determine a third confidence measure that the RFsignal data corresponds to a UAV; and aggregate the third confidencemeasure into the combined confidence measure.
 63. The system of claim62, wherein the processor is further operative to: filter the RF signaldata to remove one or more unwanted frequencies; decode the filtered RFsignal to generate a pattern of one or more frequencies and one or moreamplitudes representing the RF signal data; compare the pattern of theone or more frequencies and the one or more amplitudes representing theRF signal data to known patterns of frequencies and amplitudes known tobe associated with UAVs; and upon determination that the pattern of theone or more frequencies and the one or more amplitudes representing theRF signal data substantially matches at least one of the known patterns,determine the third confidence measure.
 64. The system of claim 61, thesystem further comprising: a Wi-Fi sensor proximate to the particularair space, wherein the Wi-Fi sensor is configured to receive Wi-Fisignal data, the Wi-Fi signal data including data indicating a possiblepresence of a UAV within the particular air space; and the processorfurther operatively coupled to the Wi-Fi sensor, the processor furtheroperative to: analyze the Wi-Fi signal data to determine a fourthconfidence measure that the Wi-Fi signal data corresponds to a UAV; andaggregate the fourth confidence measure into the combined confidencemeasure.
 65. The system of claim 64, wherein the processor is furtheroperative to: extract a media access control (MAC) address from theWi-Fi signal data; compare the extracted MAC address to one or moreknown MAC addresses known to be associated with UAVs; and; upondetermination that the extracted MAC address substantially matches atleast one known MAC address, determine the fourth confidence measure.66. The system of claim 64, wherein the processor is further operativeto: extract a service set identifier (SSID) from the Wi-Fi signal data;compare the extracted SSID to one or more known SSIDs known to beassociated with UAVs; and upon determination that the extracted SSIDsubstantially matches at least one known SSID, determining the fourthconfidence measure.
 67. The system of claim 64, wherein the processor isfurther operative to: extract a received signal strength indicator(RSSI) from the Wi-Fi signal data; and based on the extracted RSSI,estimate a physical distance of the object emanating the Wi-Fi signaldata from the particular Wi-Fi sensor; whereby the physical distancemust be above a predetermined threshold distance value to indicate thepresence of a UAV.
 68. The system of claim 61, wherein the processor isfurther operative to: identify at least one region of interest (ROI) inat least one video frame in the video data, the at least one ROIcomprising the image of the object that may be a UAV flying within theparticular air space; and perform an object classification process withrespect to the at least one ROI to determine whether the object in theimage is a UAV, the object classification process comprising the stepsof: extracting image data from the image of the at least one ROI;comparing the extracted image data to prior image data of objects knownto be UAVs to determine a probability that the object in the image is aUAV; and upon determination that the probability that the object in theimage is a UAV exceeds a predetermined threshold, determining the firstconfidence measure.
 69. The system of claim 61, wherein the processor isfurther operative to store in the database the video data and audiosignal data in association with the indication that the UAV wasidentified in the particular air space.
 70. The system of claim 61,wherein the processor is further operative to alert a system user that aUAV has been detected in the particular air space.
 71. The system ofclaim 61, wherein the predetermined threshold value comprises apercentage.
 72. The system of claim 61, wherein the video and audiosensor are enclosed in a unitary housing.